LSD (LiDAR SLAM & Detection)

LSD is an open source perception architecture for autonomous vehicle and robotics.

LSD currently supports many features:

support multiple LiDAR, camera, radar and INS/IMU sensors.
support user-friendly calibration for LiDAR and camera etc.
support software time sync, data record and playback.
support CNN based pointcloud object detection, tracking and prediction.
support GICP, FLOAM and FastLIO based frontend odometry and G2O based pose graph optimization.
support Web based interactive map correction tool(editor).
support communication with ROS.

Overview

Prerequisites

LSD can be worked both on x86 PC and Nvidia embedded boards (Xavier-NX, Xavier-AGX and Orin).

Basic Enviroment

Ubuntu20.04, Python3.8, Eigen 3.3.7, Ceres 1.14.0, Protobuf 3.8.0, NLOPT 2.4.2, G2O, OpenCV 4.5.5, PCL 1.9.1, GTSAM 4.0

Nvidia Embedded Board

The LSD is tested on Xavier-NX, Xavier-AGX and Orin with JetPack5.0.2, Installation

Getting Started

A x86_64 docker image is provided to test.

docker pull 15liangwang/auto-ipu
sudo docker run -it -d --net=host --privileged --shm-size=4g --name="LSD" -v /media:/root/exchange 15liangwang/auto-ipu
docker exec -it LSD /bin/bash

Clone this repository and build the source code

cd /home/znqc/work/
git clone https://github.com/w111liang222/lidar-slam-detection.git
cd lidar-slam-detection/
unzip slam/data/ORBvoc.zip -d slam/data/
python setup.py install

Run LSD

tools/scripts/start_system.sh

Open http://localhost (or http://localhost:1234) in your browser, e.g. Chrome, and you can see this screen.

Example Data

Download the demo data Google Drive | 百度网盘(密码sk5h) and unzip it

unzip demo_data.zip -d /home/znqc/work/
tools/scripts/start_system.sh # re-run LSD

More usages can be found here

ROS

LSD is NOT built on the Robot Operating System (ROS), but we provides some tools to bridge the communication with ROS.

rosbag proxy: a tool which send the ros topic data to LSD.
pickle to rosbag: a convenient tool to convert the pickle files which are recorded by LSD to rosbag.

License

LSD is released under the Apache 2.0 license.

Acknowledgments

In the development of LSD, we stand on the shoulders of the following repositories:

lidar_align: A simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor.
lidar_imu_calib: automatic calibration of 3D lidar and IMU extrinsics.
OpenPCDet: OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
AB3DMOT: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics.
FAST-LIO: A computationally efficient and robust LiDAR-inertial odometry package.
R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package.
FLOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization.
hdl_graph_slam: an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR.
hdl_localization: Real-time 3D localization using a (velodyne) 3D LIDAR.
ORB_SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities.
scancontext: Global LiDAR descriptor for place recognition and long-term localization.

Citation

If you find this project useful in your research, please consider cite and star this project:

@misc{LiDAR-SLAM-Detection,
    title={LiDAR SLAM & Detection: an open source perception architecture for autonomous vehicle and robotics},
    author={LiangWang},
    howpublished = {\url{https://github.com/w111liang222/lidar-slam-detection}},
    year={2023}
}

Contact

LiangWang 15lwang@alumni.tongji.edu.cn

tzeYangX/lidar-slam-detection