GuardianShell / tloam

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

The first Lidar-only odometry framework with high performance based on truncated least squares and Open3D point cloud library, The foremost improvement include:

• Fast and precision pretreatment module, multi-region ground extraction and dynamic curved-voxel clustering perform ground point extraction and category segmentation.
• Feature extraction based on principal component analysis(PCA) elaborate four distinctive feature，including: planar features, ground features, edge features, sphere features
• There are three kinds of residual functions based on truncated least squares method for directly processing above features which are point-to-point, point-to-line, and point-to-plane.
• Open3d point cloud library is integrated into SLAM algorithm framework for the first time. We extend more functions and implemented the message interface related to ROS.

[Demo Video] [Preprint Paper]

Note that regard to pure odometry without corrections through loop closures, T-LOAM delivers much less drift than F-LOAM.

Framework overview

Each frame of the 3D LiDAR is processed as input. Four main processing modules are introduced to construct the backbone of the algorithm: (a) multi-region ground extraction module, (b) dynamic curved-voxel clustering module, (c) feature extraction module, (d) pose optimization module.

Evaluation

KITTI Sequence 00	F-LOAM	T-LOAM
Translational Error(%)	1.11	0.98
Relative Error(°/100m)	0.40	0.60

Graphic Result(Path and Translation)

F-LOAM

T-LOAM

F-LOAM

T-LOAM

Dependency

-ROS(Melodic Ubuntu18.04)

sudo apt-get install python-catkin-tools ros-melodic-ecl-threads ros-melodic-jsk-recognition-msgs ros-melodic-jsk-visualization ros-melodic-velodyne-msgs

-YAML(0.6.3) Note that you must build a shared library due to we utilize the ros nodelet package.

tar -zxvf yaml-cpp-yaml-cpp-0.6.3.tar.gz
cd yaml-2.3.0 && mkdir build && cd build
cmake [-G generator] [-DYAML_BUILD_SHARED_LIBS=ON] ..
make 
sudo make install

-Open3D(A Modern Library for 3D Data Processing 0.12.0)

Please note that open3d installation will be a slightly troublesome process, please be patient. Another problem that needs attention is that Open3D-ML cannot be used in ROS at the same time due to the link error2286 and error3432. In order to fix this, you need to specify the cmake flag -DGLIBCXX_USE_CXX11_ABI=ON. However, the latest Tensorflow2.4 installed through conda(not pip) already supports the C++11 API, you can check the API with print(tensorflow.__cxx11_abi_flag__). If the flag is true, you can set the compile flag -DBUILD_TENSORFLOW_OPS=ON Next, you can complete the installation according to the instructions

cd Open3D
util/scripts/install-deps-ubuntu.sh
mkdir build && cd build 
cmake \
    -DBUILD_SHARED_LIBS=ON \
    -DPYTHON_EXECUTABLE=$(which python3) \
    -DBUILD_CUDA_MODULE=ON \
    -DGLIBCXX_USE_CXX11_ABI=ON \
    -DBUILD_LIBREALSENSE=ON  \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=/usr/local \
    -DBUILD_PYTORCH_OPS=OFF \
    -DBUILD_TENSORFLOW_OPS=OFF \
    -DBUNDLE_OPEN3D_ML=ON \
    -DOPEN3D_ML_ROOT=${replace with own Open3D-ML path} \
    ../
make -j4
sudo make install 

If you have clone problems, you can download it directly from the link below.

Baidu Disk code: khy9 or Google Drive

-Ceres Solver(A large scale non-linear optimization library 2.0) you can complete the installation according to the guide

Installation

Now create the Catkin Environment:

mkdir -p ~/tloam_ws/src
cd ~/tloam_ws
catkin init
catkin config --merge-devel
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release

And clone the project:

cd src
git clone https://github.com/zpw6106/tloam.git
catkin build

Usage

Download the KITTI Odometry Dataset, organize it according to the following structure, and modify the read path in the config/kitti/kitti_reader.yaml

-Example for running T-LOAM using the KITTI Dataset

roslaunch tloam tloam_kitti.launch

Contributors

Pengwei Zhou (Email: [e-mail])

BibTex Citation

Thank you for citing our T-LOAM paper on IEEEif you use any of this code:

@ARTICLE{9446309,
  author={Zhou, Pengwei and Guo, Xuexun and Pei, Xiaofei and Chen, Ci},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time}, 
  year={2021},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2021.3083606}
  }

Credits

We hereby recommend reading A-LOAM ,floam and TEASER for reference and thank them for making their work public.

License

The source code is released under GPLv3 license.

I am constantly working on improving this code. For any technical issues or commercial use, please contact me([e-mail]).