autoware_lidar_apollo_instance_segmentation

Purpose

This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.

Inner-workings / Algorithms

See the original design by Apollo.

Inputs / Outputs

Input

Output

Parameters

{{ json_to_markdown(“perception/autoware_lidar_apollo_instance_segmentation/schema/hdl-64.schema.json”) }}

{{ json_to_markdown(“perception/autoware_lidar_apollo_instance_segmentation/schema/vlp-16.schema.json”) }}

{{ json_to_markdown(“perception/autoware_lidar_apollo_instance_segmentation/schema/vls-128.schema.json”) }}

Assumptions / Known limits

There is no training code for CNN model.

Note

This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.

Original URL

• VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
• HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
• VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel

Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.

1. apollo 3D Obstacle Perception description

   /******************************************************************************
   * Copyright 2017 The Apollo Authors. All Rights Reserved.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *****************************************************************************/
   

1. tensorRTWrapper : It is used under the lib directory.

```txt MIT License

Copyright (c) 2018 lewes6369

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

File truncated at 100 lines see the full file

Changelog for package autoware_lidar_apollo_instance_segmentation

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.
• Contributors: Ryohsuke Mitsudome, Taekjin LEE

0.52.0 (2026-06-30)

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

• fix(clang-tidy): re-enable clang-diagnostic-unused-private-field (#12570)

  • fix(clang-tidy): re-enable unused private field diagnostic

  * Remove warning suppression for non-virtual destructors ---------

• fix(clang-tidy): re-enable clang-diagnostic-delete-non-abstract-non-virtual-dtor (#12575) fix(clang-tidy): re-enable non-virtual destructor diagnostic

• 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}.

  - localization/yabloc/{README.md,yabloc_pose_initializer/README.md}: also migrate [$HOME/autoware_map/sample-map-rosbag]{.title-ref} to [$HOME/autoware_data/maps/demos/sample-map-rosbag]{.title-ref}.

  - control/autoware_smart_mpc_trajectory_follower/README.md: migrate the [map_path:=$HOME/autoware_map/sample-map-planning]{.title-ref}

File truncated at 100 lines see the full file