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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
LSD (LiDAR SLAM & Detection) is an open source perception architecture for autonomous vehicle/robotic
| Date | Stars |
|---|---|
| 2026-07-24 | 759 |
| 2026-07-25 | 759 |
| 2026-07-28 | 759 |
| 2026-07-30 | 759 |
| 2026-08-06 | 759 |
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# LSD (LiDAR SLAM & Detection) LSD is an open source perception architecture for autonomous vehicle and robotics. <div align="left"> <img src="docs/NCLT.jpg" width = 33% /> <img src="docs/ULHK.jpg" width = 33% /> <img src="docs/home.gif" width = 30% > LSD currently supports many features: - [x] support multiple LiDAR, camera, radar and INS/IMU sensors. - [x] support user-friendly calibration for LiDAR and camera etc. - [x] support software time sync, data record and playback. - [x] support voxel 3D-CNN based pointcloud object detection, tracking and prediction. - [x] support FastLIO based frontend odometry and G2O based pose graph optimization. - [x] support Web based interactive map correction tool(editor). - [x] support communication with [ROS](#ros). # Overview - [Quick Demo](docs/demo/README.md) - [Architecture](docs/architecture.md) - [Mapping & Localization](docs/slam.md) - [Object Detection & Tracking](docs/detect.md) # 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](https://github.com/NVIDIA-AI-IOT/Lidar_AI_Solution) and the GPU with SM80+ is required.** **[2023-06-01]** [Web UI](web_ui/README.md)(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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). A x86_64 docker image is provided to test. ```bash sudo docker pull 15liangwang/lsd-cuda118 # sudo docker pull 15liangwang/lsd-cpu, 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 ```bash 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 ```bash tools/scripts/start_system.sh ``` Open http://localhost (or http://localhost:1234) in your browser, e.g. Chrome, and you can see this screen. <img src="docs/home.png" width="720pix" /> ## Example Data Download the demo data [Google Drive](https://drive.google.com/file/d/1wi3KATudMX3b4Wz0Bu-qcScaFuQDvXwW/view?usp=sharing) | [百度网盘(密码sk5h)](https://pan.baidu.com/s/1N7-w-Ls294MzfvX2X866Uw) and unzip it. (othe
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matched fp:bf994746fdc8f586, topic:robotics, topic:autonomous-driving, topic:slam