SuMa++: Efficient LiDAR-based Semantic SLAM

This repository contains the implementation of SuMa++, which generates semantic maps only using three-dimensional laser range scans.

Developed by Xieyuanli Chen and Jens Behley.

SuMa++ is built upon SuMa and RangeNet++. For more details, we refer to the original project websites SuMa and RangeNet++.

An example of using SuMa++:

Publication

If you use our implementation in your academic work, 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},
}

Dependencies

catkin
Qt5 >= 5.2.1
OpenGL >= 4.0
libEigen >= 3.2
gtsam >= 4.0

In Ubuntu 16.04: Installing all dependencies should be accomplished by

sudo apt-get install build-essential cmake libgtest-dev libeigen3-dev libboost-all-dev qtbase5-dev libglew-dev libqt5libqgtk2 catkin

Additionally, make sure you have catkin-tools and the fetch verb installed:

sudo apt install python-pip
sudo pip install catkin_tools catkin_tools_fetch empy

Build

rangenet_lib

To use SuMa++, you need to first build the rangenet_lib with the TensorRT and C++ interface. For more details about building and using rangenet_lib you could find in rangenet_lib.

SuMa++

Clone the repository in the src directory of the same catkin workspace where you built the rangenet_lib:

git clone https://github.com/PRBonn/semantic_suma.git

Download the additional dependencies (or clone glow into your catkin workspace src yourself):

catkin deps fetch

For the first setup of your workspace containing this project, you need:

catkin build --save-config -i --cmake-args -DCMAKE_BUILD_TYPE=Release -DOPENGL_VERSION=430 -DENABLE_NVIDIA_EXT=YES

Where you have to set OPENGL_VERSION to the supported OpenGL core profile version of your system, which you can query as follows:

$ glxinfo | grep "version"
server glx version string: 1.4
client glx version string: 1.4
GLX version: 1.4
OpenGL core profile version string: 4.3.0 NVIDIA 367.44
OpenGL core profile shading language version string: 4.30 NVIDIA [...]
OpenGL version string: 4.5.0 NVIDIA 367.44
OpenGL shading language version string: 4.50 NVIDIA

Here the line OpenGL core profile version string: 4.3.0 NVIDIA 367.44 is important and therefore you should use -DOPENGL_VERSION = 430. If you are unsure you can also leave it on the default version 330, which should be supported by all OpenGL-capable devices.

If you have a NVIDIA device, like a Geforce or Quadro graphics card, you should also activate the NVIDIA extensions using -DENABLE_NVIDIA_EXT=YES for info about the current GPU memory usage of the program.

After this setup steps, you can build with catkin build, since the configuration has been saved to your current Catkin profile (therefore, --save-config was needed).

Now the project root directory (e.g. ~/catkin_ws/src/semantic_suma) should contain a bin directory containing the visualizer.

How to run and use it?

Important Notice

Before running SuMa++, you need to first build the rangenet_lib and download the pretrained model.
You need to specify the model path in the configuration file in the config/ folder.
For the first time using, rangenet_lib will take several minutes to build a .trt model for SuMa++.
SuMa++ now can only work with KITTI dataset.

All binaries are copied to the bin directory of the source folder of the project. Thus,

run visualizer in the bin directory by ./visualizer,
open a Velodyne directory from the KITTI Visual Odometry Benchmark and select a ".bin" file,
start the processing of the scans via the "play button" in the GUI.

License

This project is free software made available under the MIT License. For details see the LICENSE file.

xuwuzhou/semantic_suma