ROS IndexNo version for distro humble showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro iron showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.
Recent questions tagged object_fusion at Robotics Stack Exchange
No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | MIT |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | Code Repository for the MOOC "Automated and Connected Driving Challenges" available on edX. |
| Checkout URI | https://github.com/ika-rwth-aachen/acdc.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2024-10-15 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Source code in C++ of the core object fusion functionality. Does not contain a ROS node.
Additional Links
No additional links.
Maintainers
- ACDC
Authors
No additional authors.
Object Fusion
This package contains a library that implements a multi-instance Kalman filter for object-level sensor data fusion and tracking. An overview over the functionality is given in ../README.md.
Mathematical symbols
The Kalman filter symbols in the code are:
| Symbol in code | Meaning | Symbol in ACDC slides, remarks |
|---|---|---|
x_hat_G |
Global object state vector | $\hat{x}_g$ |
globalObject.P() |
Global object state vector error covariance | $\mathbf{P_G}$ |
F |
State transition matrix (motion model matrix) | $\mathbf{F}$ |
Q |
Process noise matrix (adds noise during prediction) | $\mathbf{Q}$ |
C |
Measurement matrix (reduces global state space to measured space) | $\mathbf{C}$ |
x_hat_S |
Vector of measured and non-measured variables of the sensor-level object | $\hat{x}_S$, to get it in code: IkaUtilities::getEigenStateVec(&measuredObject)
|
z |
Vector of actually measured variables (in the measured space) | $\mathbf{z}$ |
P_S_diag |
Variance vector of measured and non-measured variables of the sensor-level object | $\mathbf{P_S}$, but only its diagonal in the code: IkaUtilities::getEigenVarianceVec(&measuredObject)
|
R |
Measured variables error covariance matrix | $\mathbf{R}$ |
S |
Innovation error covariance (or residual error covariance) | $\mathbf{S}$ |
K |
Kalman gain | $\mathbf{K}$ |
CHANGELOG
No CHANGELOG found.
Dependant Packages
| Name | Deps |
|---|---|
| object_fusion_wrapper |
Launch files
No launch files found
Messages
No message files found.
Services
No service files found
Plugins
No plugins found.