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开源软件名称:RobustFieldAutonomyLab/LeGO-LOAM开源软件地址:https://github.com/RobustFieldAutonomyLab/LeGO-LOAM开源编程语言:C++ 99.0%开源软件介绍:LeGO-LOAMThis repository contains code for a lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for ROS compatible UGVs. The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontally) and optional IMU data as inputs. It outputs 6D pose estimation in real-time. A demonstration of the system can be found here -> https://www.youtube.com/watch?v=O3tz_ftHV48 Lidar-inertial OdometryAn updated lidar-initial odometry package, LIO-SAM, has been open-sourced and available for testing. Dependency
CompileYou can use the following commands to download and compile the package.
When you compile the code for the first time, you need to add "-j1" behind "catkin_make" for generating some message types. "-j1" is not needed for future compiling. The systemLeGO-LOAM is speficifally optimized for a horizontally placed VLP-16 on a ground vehicle. It assumes there is always a ground plane in the scan. The UGV we are using is Clearpath Jackal. It has a built-in IMU. The package performs segmentation before feature extraction. Lidar odometry performs two-step Levenberg Marquardt optimization to get 6D transformation. New LidarThe key thing to adapt the code to a new sensor is making sure the point cloud can be properly projected to an range image and ground can be correctly detected. For example, VLP-16 has a angular resolution of 0.2° and 2° along two directions. It has 16 beams. The angle of the bottom beam is -15°. Thus, the parameters in "utility.h" are listed as below. When you implement new sensor, make sure that the ground_cloud has enough points for matching. Before you post any issues, please read this.
Another example for Velodyne HDL-32e range image projection:
New: a new useCloudRing flag has been added to help with point cloud projection (i.e., VLP-32C, VLS-128). Velodyne point cloud has "ring" channel that directly gives the point row id in a range image. Other lidars may have a same type of channel, i.e., "r" in Ouster. If you are using a non-Velodyne lidar but it has a similar "ring" channel, you can change the PointXYZIR definition in utility.h and the corresponding code in imageProjection.cpp. For KITTI users, if you want to use our algorithm with HDL-64e, you need to write your own implementation for such projection. If the point cloud is not projected properly, you will lose many points and performance. If you are using your lidar with an IMU, make sure your IMU is aligned properly with the lidar. The algorithm uses IMU data to correct the point cloud distortion that is cause by sensor motion. If the IMU is not aligned properly, the usage of IMU data will deteriorate the result. Ouster lidar IMU is not supported in the package as LeGO-LOAM needs a 9-DOF IMU. Run the package
Notes: The parameter "/use_sim_time" is set to "true" for simulation, "false" to real robot usage.
Notes: Though /imu/data is optinal, it can improve estimation accuracy greatly if provided. Some sample bags can be downloaded from here. New data-setThis dataset, Stevens data-set, is captured using a Velodyne VLP-16, which is mounted on an UGV - Clearpath Jackal, on Stevens Institute of Technology campus. The VLP-16 rotation rate is set to 10Hz. This data-set features over 20K scans and many loop-closures. Cite LeGO-LOAMThank you for citing our LeGO-LOAM paper if you use any of this code:
Loop ClosureThe loop-closure method implemented in this package is a naive ICP-based method. It often fails when the odometry drift is too large. For more advanced loop-closure methods, there is a package called SC-LeGO-LOAM, which features utilizing point cloud descriptor. Speed OptimizationAn optimized version of LeGO-LOAM can be found here. All credits go to @facontidavide. Improvements in this directory include but not limited to:
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