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ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

ros2go2estimator repository

fusion_estimator

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/shineminxing/ros2go2estimator.git
VCS Type git
VCS Version main
Last Updated 2026-04-02
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

Name Version
fusion_estimator 0.0.0

README

CAPO-LeggedRobotOdometry 🦾 License

Language / θ―­θ¨€οΌš English δΈ­ζ–‡

CAPO-LeggedRobotOdometry is a ROS 2 (Humble) proprioceptive odometry / state-estimation repository for biped / quadruped / wheel-legged robots on Ubuntu 22.04.

It provides high-accuracy odometry using only IMU + joint encoders + foot contact/force signals, without requiring cameras or LiDAR.

In 3D closed-loop trials (a 200 m horizontal and 15 m vertical loop), Astrall point-foot robot A achieves 0.1638 m horizontal error and 0.219 m vertical error; for wheel-legged robot B, the corresponding errors are 0.2264 m and 0.199 m.

At the repository level, fusion_estimator_node.cpp is mainly the ROS2 wrapper (topics, parameters, message conversion, publishing), while the actual fusion-estimation algorithm lives in FusionEstimator/ as a portable, pure C++ implementation. This makes it straightforward to port the estimator to ROS1, non-ROS2 applications, or embedded platforms.

In addition, the Matlab/ folder provides a MATLAB + MEX example that compiles and calls the same C++ estimator core for offline validation, algorithm analysis, and cross-platform reuse.

πŸ“„ Paper

Contact-Anchored Proprioceptive Odometry for Quadruped Robots (arXiv:2602.17393)

  • Paper: https://arxiv.org/abs/2602.17393

If you use this repository in research, please consider citing the paper.

πŸ“¦ Data Sharing (Go2-EDU ROS bags)

To help readers quickly validate the pipeline, we provide two Go2-EDU trial datasets, including real-world videos and the corresponding ROS bag topics/messages required by this node, enabling fast reproduction and sanity checks.

  • Download (Google Drive): https://drive.google.com/drive/folders/1FfVO69rfmUu6B9crPhZCfKf9wFnV4L7n?usp=sharing

Note: the IMU on this Go2-EDU platform exhibits noticeable yaw drift, so the odometry accuracy is generally worse than the results reported for Astrall robots A and B in the paper.

✨ Key Features

Category Description
Biped / Quadruped / Wheel-Legged Unified Online contact-set switching; stance legs are detected automatically, supporting fast transitions between standing and walking.
Full 3D & Planar 2D Publishes both 6DoF odometry (SMX/Odom) and a gravity-flattened 2D odometry (SMX/Odom_2D).
No Exteroception Required Works without cameras or LiDAR; only IMU, joint encoders, and foot contact/force signals are required.
Portable Pure C++ Core The estimator core is isolated in FusionEstimator/, making it easier to reuse outside ROS2.
MATLAB / MEX Validation The Matlab/ folder demonstrates how to compile and invoke the same C++ core from MATLAB.
Runtime Tuning Key parameters can be adjusted through config.yaml, and platform-dependent thresholds can be tuned for different robots.
Scope Repository Summary
Low-level / Drivers https://github.com/ShineMinxing/Ros2Go2Base DDS bridge, Unitree SDK2 control, pointcloud→LaserScan, TF utilities
Odometry CAPO-LeggedRobotOdometry (this repo) Pure proprioceptive fusion, publishes SMX/Odom / SMX/Odom_2D
SLAM / Mapping https://github.com/ShineMinxing/Ros2SLAM Integrations for Cartographer 3D, KISS-ICP, FAST-LIO2, Point-LIO
Voice / LLM https://github.com/ShineMinxing/Ros2Chat Offline ASR + OpenAI Chat + TTS
Vision https://github.com/ShineMinxing/Ros2ImageProcess Camera pipelines, spot / face / drone detection
Gimbal Tracking https://github.com/ShineMinxing/Ros2AmovG1 Amov G1 gimbal control and tracking
Tools https://github.com/ShineMinxing/Ros2Tools Bluetooth IMU, joystick mapping, gimbal loop, data logging

⚠️ Clone as needed. If you only need state estimation, this repository is sufficient. For mapping, it is natural to pair it with Ros2SLAM and Ros2Go2Base.

πŸ“‚ Repository Layout

CAPO-LeggedRobotOdometry/
β”œβ”€β”€ CMakeLists.txt
β”œβ”€β”€ package.xml
β”œβ”€β”€ config.yaml
β”œβ”€β”€ fusion_estimator_node.cpp        # ROS2 node wrapper: params / topics / odom publishing
β”œβ”€β”€ FusionEstimator/                 # pure C++ fusion-estimation core (portable)
β”‚   β”œβ”€β”€ Estimators/
β”‚   β”œβ”€β”€ fusion_estimator.h
β”‚   β”œβ”€β”€ LowlevelState.h
β”‚   β”œβ”€β”€ SensorBase.cpp
β”‚   β”œβ”€β”€ SensorBase.h
β”‚   β”œβ”€β”€ Sensor_IMU.cpp
β”‚   β”œβ”€β”€ Sensor_IMU.h
β”‚   β”œβ”€β”€ Sensor_Legs.cpp
β”‚   β”œβ”€β”€ Sensor_Legs.h
β”‚   └── Readme.md
β”œβ”€β”€ Matlab/                          # MATLAB + MEX example for calling the C++ core
β”‚   β”œβ”€β”€ build_mex.m
β”‚   β”œβ”€β”€ fusion_estimator.m
β”‚   β”œβ”€β”€ fusion_estimator_mex.cpp
β”‚   β”œβ”€β”€ MPXY150Z10.csv
β”‚   └── MWXY150Z10.csv
β”œβ”€β”€ Plotjuggler.xml                  # PlotJuggler layout / visualization helper
└── Readme.md

🧩 Architecture Notes

This repository is intentionally split into two layers:

File truncated at 100 lines see the full file

CONTRIBUTING