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

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  
nv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.x.x.x-ga-yyyymmdd_1-1_amd64.deb
$ sudo apt-key add /var/nv-tensorrt-repo-cudax.x-trt5.x.x.x-ga-yyyymmdd/7fa2af80.pub

$ sudo apt-get update
$ sudo apt-get install tensorrt

4. Download the pretrained file from here.

$ git clone [email protected]:cirpue49/kitti_pretrained_point_pillars.git

• 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);

• 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. (https://creativecommons.org/licenses/by-nc-sa/3.0/)