autoware_lidar_frnet

Purpose

The autoware_lidar_frnet package is used for 3D semantic segmentation based on LiDAR data (x, y, z, intensity).

Inner-workings / Algorithms

The implementation is based on the FRNet [1] project. It uses TensorRT library for data processing and network inference.

We trained the models using AWML [2].

Inputs / Outputs

Input

Output

Parameters

FRNet node

{{ json_to_markdown(“perception/autoware_lidar_frnet/schema/frnet.schema.json”) }}

FRNet model

{{ json_to_markdown(“perception/autoware_lidar_frnet/schema/ml_package_frnet.schema.json”) }}

FRNet diagnostics

{{ json_to_markdown(“perception/autoware_lidar_frnet/schema/diagnostics_frnet.schema.json”) }}

The build_only option

The autoware_lidar_frnet node has build_only option to build the TensorRT engine file from the ONNX file.

ros2 launch autoware_lidar_frnet lidar_frnet.launch.xml build_only:=true

Assumptions / Known limits

This library operates on raw cloud data (bytes). It supports multiple input pointcloud formats and automatically detects the format on the first received message. The supported formats are (checked in order from largest to smallest, with exact field count match required):

XYZIRCAEDT (10 fields)

[
  sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1),
  sensor_msgs.msg.PointField(name='return_type', offset=13, datatype=2, count=1),
  sensor_msgs.msg.PointField(name='channel', offset=14, datatype=4, count=1),
  sensor_msgs.msg.PointField(name='azimuth', offset=16, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='elevation', offset=20, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='distance', offset=24, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='time_stamp', offset=28, datatype=6, count=1)
]

XYZIRADRT (9 fields)

[
  sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='ring', offset=16, datatype=4, count=1),
  sensor_msgs.msg.PointField(name='azimuth', offset=18, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='distance', offset=22, datatype=7, count=1),
  sensor_msgs.msg.PointField(name='return_type', offset=26, datatype=2, count=1),
  sensor_msgs.msg.PointField(name='time_stamp', offset=27, datatype=8, count=1)
]

XYZIRC (6 fields)

```python [ sensor_msgs.msg.PointField(name=’x’, offset=0, datatype=7, count=1), sensor_msgs.msg.PointField(name=’y’, offset=4, datatype=7, count=1),

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Changelog for package autoware_lidar_frnet

0.53.0 (2026-09-29)

• Merge remote-tracking branch 'origin/main' into prepare-0.53.0-changelog
• fix(design): align the perception node designs with the packages they describe (#13339) The lanelet filter components register under the lanelet_filter:: namespace, and the apollo instance segmentation and transfusion nodes are built with the autoware_ prefix on the executable name. BEVFusion, StreamPetr and LidarFRNet name param file defaults that resolve to nothing: the first two are missing the config/ prefix, and the three files LidarFRNet names are called frnet.param.yaml, ml_package_frnet_ot128.param.yaml and diagnostics_frnet.param.yaml. ElevationMapLoader pins its map_hash input with global:, which keeps the port out of the design graph: link_manager skips any connection whose target is a global input port and the exporter emits no remap. remap_target: keeps the same fixed topic name while letting the port take part in the graph.
• refactor(autoware_universe): use autoware_ament_auto_package in perception DNN packages (#12277) Co-authored-by: github-actions <<github-actions@github.com>> Co-authored-by: Taekjin LEE <<taekjin.lee@tier4.jp>>
• refactor(perception): move node design files into each package (#13104) Co-authored-by: Claude Opus 5 (1M context) <<noreply@anthropic.com>>
• fix(autoware_lidar_frnet): sync configs (#13040)
• Contributors: Amadeusz Szymko, Ryohsuke Mitsudome, Taekjin LEE, Vishal Chauhan

0.52.0 (2026-06-30)

• Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base

• fix(clang-tidy): fix unchecked optional access in lidar frnet (#12614)

  • fix(clang-tidy): fix unchecked optional access in lidar frnet

  * Address optional access review comments ---------

• Contributors: Vishal Chauhan, github-actions

0.51.0 (2026-05-01)

• Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base

• feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):

  • e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
  • localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
  • perception/autoware_bevfusion/launch/bevfusion.launch.xml
  • perception/autoware_camera_streampetr/launch/streampetr.launch.xml
  • perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
  • perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
  • perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
  • perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
  • perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
  • perception/autoware_ptv3/launch/ptv3.launch.xml
  • perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
  • perception/autoware_simpl_prediction/launch/simpl.launch.xml
  • perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
  • perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
  • perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
  • perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
  • perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
  • planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml

  - sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:

  - e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also migrate the [$HOME/autoware_map/Town01]{.title-ref} examples to [$HOME/autoware_data/maps/Town01]{.title-ref}.

  -

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