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 voxel 3D-CNN based pointcloud object detection, tracking and prediction.
• support FastLIO based frontend odometry and G2O based pose graph optimization.
• support Web based interactive map correction tool(editor).
• support communication with ROS.

Overview

• Quick Demo
• Architecture
• Mapping & Localization
• Object Detection & Tracking

Changelog

[2024-12-21] LiDAR mapping (LIO odometry and PGO) is further improved.

• LIO odometry: degenerate detection and wheelspeed observation for FASTLIO.
• PGO backend: GNSS outlier detection and extrinsic estimation.
• Map management: support automatic multi-maps merging.
• Other: pointcloud colouration with multi-cam, dense map generation and pointcloud to BEV (under tools/postprocessing).
• BUG fix: UTM projection when cross the projection zone and the grid converage of UTM is considered.

[2023-10-08] Better 3DMOT (GIOU, Two-stage association).

Performance (WOD val)	AMOTA ↑	AMOTP ↓	IDs(%) ↓
AB3DMOT	47.84	0.2584	0.67
GIOU + Two-stage	54.79	0.2492	0.19

[2023-07-06] A new detection model (CenterPoint-VoxelNet) is support to run realtime (30FPS+).

Performance (WOD val)	Vec_L1	Vec_L2	Ped_L1	Ped_L2	Cyc_L1	Cyc_L2
PointPillar	73.71/73.12	65.71/65.17	71.70/60.90	63.52/53.78	65.30/63.77	63.12/61.64
CenterPoint-VoxelNet (1 frame)	74.75/74.24	66.09/65.63	77.66/71.54	68.57/63.02	72.03/70.93	69.63/68.57
CenterPoint-VoxelNet (4 frame)	77.55/77.03	69.65/69.17	80.72/77.80	72.91/70.15	72.63/71.72	70.55/69.67

Note: the CenterPoint-VoxelNet is built on libspconv and the GPU with SM80+ is required.

[2023-06-01] Web UI(JS code of preview, tviz and map editor) is uploaded.

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

Getting Started

NVIDIA Container Toolkit is needed to install firstly Installation.

A x86_64 docker image is provided to test.

sudo docker pull 15liangwang/lsd-cuda118 # sudo docker pull 15liangwang/auto-ipu, if you don't have GPU
sudo docker run --gpus all -it -d --net=host --privileged --shm-size=4g --name="LSD" -v /media:/root/exchange 15liangwang/lsd-cuda118
sudo 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
bash sensor_inference/pytorch_model/export/generate_trt.sh

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. (other dataset can be found 百度网盘, 提取码：36ly)

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 to pickle: convert rosbag to pickle files, then LSD can read and run.
• pickle to rosbag: a convenient tool to convert the pickle files which are recorded by LSD to rosbag.
• rosbag proxy: a tool which send the ros topic data to LSD.

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