Last edited by Harold_zhang In 2020-12-9 11:29 Editor
1 Livox 上位机 / Livox Software
1.1 上位机软件下载 / Livox lidar software download资料说明/ Description: Livox Viewer 0.10.0 (64bit) – Windows 7 / 10 Livox Viewer 0.10.0 (64bit) – Ubuntu 16.04 / 18.04
1.2 Livox Viewer 用户手册 / Livox Viewer user manual
2 Livox SDK / Ros driver
2.1 SDK下载链接 / SDK download资料说明 / Description: 支持Windows 7/10 64bit / Ubuntu 16.04 / 18.04 64bit Support Windows 7/10 64bit / Ubuntu 16.04 / 18.04 64bit
2.2 ROS平台的Livox ros driver / Livox ros driver on ROS platform资料说明 / Description: 在ROS平台上使用的Livox 雷达驱动的安装和使用指南 Livox driver on ROS platform and the installation and user guide
2.3 SDK通讯协议和数据格式 / SDK Communication protocol and data format资料说明 / Description: 通讯协议格式 / Communication protocol 同步和时间戳格式 / Time synchronization and timestamp data format 点云数据格式 / Point cloud data format 雷达状态代码参考 / Lidar status code references
3 开源算法/ Open source algorithm
3.1 Livox_Mapping It’s a mapping package for Livox LiDARs. The package currently contains the basic functions of low-speed mapping.
3.2 Horizon_Highway_Slam It’s a robust, low drift, and real time highway SLAM package suitable for the LivoxHorizonlidar, which is a high-performance LiDAR sensor built for Level 3 and Level 4 autonomous driving.
3.3 Livox-Horizon-LOAM It’s a robust, low drift, and real time odometry and mapping package for Livox LiDARs, significant low cost and high performance LiDARs that are designed for massive industrials uses. Our package is mainly designed for low-speed scenes(~5km/h)
3.4 Livox_Camera_Lidar_Calibration This solution provides a method for manually calibrating the extrinsic parameters between Livox LiDAR and camera, which has been verified on series Mid-40, Horizon and Tele-15.
3.5 Livox_Detection Livox Detection is a robust,real time detection package for Livox LiDARs. The detector is designed for L3 and L4 autonomous driving. It can effectively detect within 200*100m range under different vehicle speed conditions(0~120km/h).
3.6 Livox_Automatic_Calibration This technology mainly relies on the isomorphic constraint assumption model of the environment to realize automatic calibration, and only needs to use the original point cloud data of the base LiDAR and target LiDAR.
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