Rangenet Library

This repository contains simple usage explanations of how the RangeNet++ inference works with the TensorRT and C++ interface.

Developed by Xieyuanli Chen, Andres Milioto and Jens Behley.

For more details about RangeNet++, one could find in LiDAR-Bonnetal.

How to use

Dependencies

System dependencies

First you need to install the nvidia driver and CUDA.

  • CUDA Installation guide: Link

  • Then you can do the other dependencies:

    $ sudo apt-get update 
$ sudo apt-get install -yqq  build-essential python3-dev python3-pip apt-utils git cmake libboost-all-dev libyaml-cpp-dev libopencv-dev
Python dependencies

  • Then install the Python packages needed:

    $ sudo apt install python-empy
$ sudo pip install catkin_tools trollius numpy
TensorRT

In order to infer with TensorRT during inference with the C++ libraries:

  • Install TensorRT: Link.
  • Our code now only works with TensorRT version 5 (Note that you need at least version 5.1.0).

Build the library

We use the catkin tool to build the library.

$ mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws/src
$ git clone https://github.com/ros/catkin.git 
$ git clone https://github.com/PRBonn/rangenet_lib.git
$ cd .. && catkin init
$ catkin build rangenet_lib

Run the demo

To run the demo, you need a pre-trained model, which can be downloaded here, model.

A single LiDAR scan for running the demo, you could find in the example folder example/000000.bin. For more LiDAR data, you could download from KITTI odometry dataset.

For more details about how to train and evaluate a model, please refer to LiDAR-Bonnetal.

To infer a single LiDAR scan and visualize the semantic point cloud:

# go to the root path of the catkin workspace
$ cd ~/catkin_ws
# use --verbose or -v to get verbose mode
$ ./devel/lib/rangenet_lib/infer -h # help
$ ./devel/lib/rangenet_lib/infer -p /path/to/the/pretrained/model -s /path/to/the/scan.bin --verbose

Notice: for the first time running, it will take several minutes to generate a .trt model for C++ interface.

Run SuMa++: Efficient LiDAR-based Semantic SLAM

Using rangenet_lib, we built a LiDAR-based Semantic SLAM system, called SuMa++.

You could find more implementation details in SuMa++.

Citations

If you use this library for any academic work, please cite the original paper.

@inproceedings{milioto2019iros,
  author    = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
  title     = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
  booktitle = {IEEE/RSJ Intl.~Conf.~on Intelligent Robots and Systems (IROS)},
  year      = 2019,
  codeurl   = {https://github.com/PRBonn/lidar-bonnetal},
  videourl  = {https://youtu.be/wuokg7MFZyU},
}

If you use SuMa++, please cite the corresponding paper:

@inproceedings{chen2019iros, 
  author    = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
  title     = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
  booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
  year      = {2019},
  codeurl   = {https://github.com/PRBonn/semantic_suma/},
  videourl  = {https://youtu.be/uo3ZuLuFAzk},
}

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