Skip to content
Branch: master
Find file History
Latest commit a4b6f37 Mar 20, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
include/lidar_point_pillars [Feature]PointPillars (#2029) Mar 8, 2019
launch [Feature]PointPillars (#2029) Mar 8, 2019
nodes [Feature]PointPillars (#2029) Mar 8, 2019
test [Feature]PointPillars (#2029) Mar 8, 2019
CHANGELOG.rst 1.11.0 Mar 21, 2019
CMakeLists.txt [Feature]PointPillars (#2029) Mar 8, 2019 [Feature]PointPillars (#2029) Mar 8, 2019
package.xml 1.11.0 Mar 21, 2019

Point Pillars for 3D Object Detection: ver. 1.0

Autoware package for Point Pillars. Referenced paper.


CUDA Toolkit v9.0 or v10.0

CUDNN: Tested with v7.3.1

TensorRT: Tested with 5.0.2 -> How to install

How to setup

  1. Install CUDA from this website

  2. Install CUDNN

  3. Download the TensorRT local repo file that matches the Ubuntu version you are using.

  4. Install TensorRT from the Debian local repo package.

$ sudo dpkg -i  
$ sudo apt-key add /var/nv-tensorrt-repo-cudax.x-trt5.x.x.x-ga-yyyymmdd/

$ sudo apt-get update
$ sudo apt-get install tensorrt
  1. Download the pretrained file from here.
$ git clone

How to launch

  • Launch file: roslaunch lidar_point_pillars lidar_point_pillars.launch pfe_onnx_file:=/PATH/TO/FILE.onnx rpn_onnx_file:=/PATH/TO/FILE.onnx input_topic:=/points_raw

  • You can launch it through the runtime manager in Computing tab, as well.


* @brief Call PointPillars for the inference.
* @param[in] in_points_array pointcloud array
* @param[in] in_num_points Number of points
* @param[out] out_detections Output bounding box from the network
* @details This is an interface for the algorithm.
void doInference(float* in_points_array, int in_num_points, std::vector<float> out_detections);


Parameter Type Description Default
input_topic String Input topic Pointcloud. /points_raw
baselink_support Bool Whether to use baselink to adjust parameters. True
reproduce_result_mode Bool Whether to enable reproducible result mode at the cost of the runtime. False
score_threshold Float Minimum score required to include the result [0,1] 0.5
nms_overlap_threshold Float Minimum IOU required to have when applying NMS [0,1] 0.5
pfe_onnx_file String Path to the PFE onnx file
rpn_onnx_file String Path to the RPN onnx file


Topic Type Description
/detection/lidar_detector/objects autoware_msgs/DetectedObjetArray Array of Detected Objects in Autoware format


  • To display the results in Rviz objects_visualizer is required. (Launch file launches automatically this node).

  • Pretrained models are available here, trained with the help of the KITTI dataset. For this reason, these are not suitable for commercial purposes. Derivative works are bound to the BY-NC-SA 3.0 License. (

You can’t perform that action at this time.