Ground Segmentation Package in ROS.
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Updated
Oct 9, 2019 - Python
Ground Segmentation Package in ROS.
Segmentation of a video frame into ground and upright objects using sparse and dense optical flow techniques in OpenCV.
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.
A C++ version for "A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles" 2018 ITSC
An implementation on "Shen Z, Liang H, Lin L, Wang Z, Huang W, Yu J. Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process. Remote Sensing. 2021; 13(16):3239. https://doi.org/10.3390/rs13163239"
A ground segmentation algorithm for 3D point clouds based on the work described in “Fast segmentation of 3D point clouds: a paradigm on LIDAR data for Autonomous Vehicle Applications”, D. Zermas, I. Izzat and N. Papanikolopoulos, 2017. Distinguish between road and non-road points. Road surface extraction. Plane fit ground filter
Efficient Online Segmentation of Ground&Wall Points for Multi-line Spinning LiDARs. //在线分割激光点云中的地面点和墙面点。
Pointcloud Ground Segmentation: python binding of url-kaist/TRAVEL
Ground segmentation benchmark in SemanticKITTI dataset
ROS2 Implementation of Patchwork++
ROS2 Implementation of Patchwork
Pure Python Libary of Ground Segmentation Algorithms
ROS & ROS2 Implementation of Patchwork++
Python Package: Fast Ground Segmentation for LiDAR Point Clouds
SOTA fast and robust ground segmentation using 3D point cloud (accepted in RA-L'21 w/ IROS'21)
Patchwork++: Fast and robust ground segmentation method for 3D LiDAR scans. @ IROS'22
快速3D点云分割论文代码(带注解):Fast segmentation of 3d point clouds for ground vehicles
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
点云分割论文2017 Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications
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