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autoware_lidar_apollo_instance_segmentation package from autoware_universe repo

autoware_adapi_specs autoware_agnocast_wrapper autoware_auto_common autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower 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autoware_simple_object_merger autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_mission_planner_universe autoware_obstacle_cruise_planner autoware_obstacle_stop_planner autoware_path_optimizer autoware_path_smoother autoware_planning_validator autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model 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autoware_mission_details_overlay_rviz_plugin autoware_overlay_rviz_plugin autoware_string_stamped_rviz_plugin autoware_perception_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_datetime_rviz_plugin tier4_localization_rviz_plugin tier4_planning_factor_rviz_plugin tier4_planning_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

Package Summary

Tags No category tags.
Version 0.43.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-04-04
Dev Status UNMAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

autoware_lidar_apollo_instance_segmentation

Additional Links

No additional links.

Maintainers

  • Yoshi Ri
  • Yukihiro Saito

Authors

  • Kosuke Takeuchi
  • Yukihiro Saito

autoware_lidar_apollo_instance_segmentation

Peek 2020-04-07 00-17

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

Name Type Description
input/pointcloud sensor_msgs/PointCloud2 Pointcloud data from lidar sensors

Output

Name Type Description
output/labeled_clusters tier4_perception_msgs/DetectedObjectsWithFeature Detected objects with labeled pointcloud cluster.
debug/instance_pointcloud sensor_msgs/PointCloud2 Segmented pointcloud for visualization.

Parameters

Node Parameters

None

Core Parameters

Name Type Default Value Description
score_threshold double 0.8 If the score of a detected object is lower than this value, the object is ignored.
range int 60 Half of the length of feature map sides. [m]
width int 640 The grid width of feature map.
height int 640 The grid height of feature map.
engine_file string “vls-128.engine” The name of TensorRT engine file for CNN model.
prototxt_file string “vls-128.prototxt” The name of prototxt file for CNN model.
caffemodel_file string “vls-128.caffemodel” The name of caffemodel file for CNN model.
use_intensity_feature bool true The flag to use intensity feature of pointcloud.
use_constant_feature bool false The flag to use direction and distance feature of pointcloud.
target_frame string “base_link” Pointcloud data is transformed into this frame.
z_offset int 2 z offset from target frame. [m]
build_only bool false shutdown the node after TensorRT engine file is built

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

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.
   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
   SOFTWARE.
   
  1. autoware_perception description
   /*
   * Copyright 2018-2019 Autoware Foundation. 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.
   */
   

Special thanks

CHANGELOG

Changelog for package autoware_lidar_apollo_instance_segmentation

0.43.0 (2025-03-21)

  • Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
  • chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
  • refactor: add autoware_cuda_dependency_meta (#10073)
  • Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

  • Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
  • feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
  • Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

  • chore: bump version to 0.41.1 (#10088)
  • Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

  • Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
  • feat(autoware_lidar_apollo_instance_segmentation): tier4_debug_msgs to autoware_internal_debug_msgs in files perce… (#9876) feat: tier4_debug_msgs to autoware_internal_debug_msgs in files perception/autoware_lidar_apollo_instance_segmentation Co-authored-by: Ryohsuke Mitsudome <<43976834+mitsudome-r@users.noreply.github.com>>
  • refactor(autoware_tensorrt_common): multi-TensorRT compatibility & tensorrt_common as unified lib for all perception components (#9762)
    • refactor(autoware_tensorrt_common): multi-TensorRT compatibility & tensorrt_common as unified lib for all perception components
    • style(pre-commit): autofix
    • style(autoware_tensorrt_common): linting

    * style(autoware_lidar_centerpoint): typo Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>> * docs(autoware_tensorrt_common): grammar Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>

    • fix(autoware_lidar_transfusion): reuse cast variable
    • fix(autoware_tensorrt_common): remove deprecated inference API

    * style(autoware_tensorrt_common): grammar Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>> * style(autoware_tensorrt_common): grammar Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>

    • fix(autoware_tensorrt_common): const pointer
    • fix(autoware_tensorrt_common): remove unused method declaration
    • style(pre-commit): autofix

    * refactor(autoware_tensorrt_common): readability Co-authored-by: Kotaro Uetake <<60615504+ktro2828@users.noreply.github.com>>

    • fix(autoware_tensorrt_common): return if layer not registered

    * refactor(autoware_tensorrt_common): readability Co-authored-by: Kotaro Uetake <<60615504+ktro2828@users.noreply.github.com>>

    • fix(autoware_tensorrt_common): rename struct

    * style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]\@users.noreply.github.com> Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>> Co-authored-by: Kotaro Uetake <<60615504+ktro2828@users.noreply.github.com>>

  • Contributors: Amadeusz Szymko, Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

  • Merge branch 'main' into release-0.40.0
  • Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
  • fix: fix ticket links in CHANGELOG.rst (#9588)
  • chore(package.xml): bump version to 0.39.0 (#9587)
    • chore(package.xml): bump version to 0.39.0
    • fix: fix ticket links in CHANGELOG.rst

    * fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>

  • fix: fix ticket links in CHANGELOG.rst (#9588)
  • fix(cpplint): include what you use - perception (#9569)
  • 0.39.0
  • update changelog
  • Merge commit '6a1ddbd08bd' into release-0.39.0
  • fix: fix ticket links to point to https://github.com/autowarefoundation/autoware_universe (#9304)
  • fix: fix ticket links to point to https://github.com/autowarefoundation/autoware_universe (#9304)
  • chore(package.xml): bump version to 0.38.0 (#9266) (#9284)
    • unify package.xml version to 0.37.0
    • remove system_monitor/CHANGELOG.rst
    • add changelog

    * 0.38.0

  • fix(autoware_lidar_apollo_instance_segmentation): fix cppcheck suspiciousFloatingPointCast (#9195)
  • refactor(cuda_utils): prefix package and namespace with autoware (#9171)
  • Contributors: Esteve Fernandez, Fumiya Watanabe, M. Fatih Cırıt, Ryohsuke Mitsudome, Ryuta Kambe, Yutaka Kondo

0.39.0 (2024-11-25)

0.38.0 (2024-11-08)

  • unify package.xml version to 0.37.0
  • refactor(tensorrt_common)!: fix namespace, directory structure & move to perception namespace (#9099)
    • refactor(tensorrt_common)!: fix namespace, directory structure & move to perception namespace
    • refactor(tensorrt_common): directory structure
    • style(pre-commit): autofix

    * fix(tensorrt_common): correct package name for logging ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]\@users.noreply.github.com> Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>

  • fix(autoware_lidar_apollo_instance_segmentation): added existence probability (#8862)
    • added existence probability

    * style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]\@users.noreply.github.com>

  • fix(lidar_apollo_instance_segmentation): fix critical bug (#8444) Co-authored-by: Shintaro Tomie <<58775300+Shin-kyoto@users.noreply.github.com>>
  • refactor(lidar_apollo_instance_segmentation)!: fix namespace and directory structure (#7995)
    • refactor: add autoware namespace prefix
    • chore: update CODEOWNERS

    * refactor: add [autoware]{.title-ref} prefix ---------

  • Contributors: Amadeusz Szymko, Kotaro Uetake, Samrat Thapa, Yutaka Kondo, kminoda

0.26.0 (2024-04-03)

Wiki Tutorials

This package does not provide any links to tutorials in it's rosindex metadata. You can check on the ROS Wiki Tutorials page for the package.

Launch files

  • launch/lidar_apollo_instance_segmentation.launch.xml
      • input/pointcloud [default: /sensing/lidar/pointcloud]
      • model [default: model_128]
      • output/objects [default: labeled_clusters]
      • build_only [default: false]
      • base_name [default: vlp-16]
      • base_name [default: hdl-64]
      • base_name [default: vls-128]
      • data_path [default: $(env HOME)/autoware_data]
      • trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
      • param_file [default: $(find-pkg-share autoware_lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
      • target_frame [default: base_link]
      • z_offset [default: -2.0]
      • precision [default: fp32]

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_lidar_apollo_instance_segmentation at Robotics Stack Exchange

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