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lidar-segmentation-based-on-range-image's Introduction

LIDAR-Segmentation-Based-on-Range-Image

build passingvelodyne_HDL_32E compliant

This is a lidar segmentation method based on range-image.

Method

  1. The ground remove method is from "D. Zermas, I. Izzat and N. Papanikolopoulos, "Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 5067-5073, doi: 10.1109/ICRA.2017.7989591."

  2. The scan line compensation method is from "P. Burger and H. Wuensche, "Fast Multi-Pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles," 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, 2018, pp. 2150-2156, doi: 10.1109/IVS.2018.8500552."

  3. The range image segmentation method is from "I. Bogoslavskyi and C. Stachniss, "Fast range image-based segmentation of sparse 3D laser scans for online operation," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016, pp. 163-169, doi: 10.1109/IROS.2016.7759050."

  4. The hash table method is inspired by "S. Park, S. Wang, H. Lim and U. Kang, "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 6459-6464, doi: 10.1109/IROS40897.2019.8968026."

  5. The process of segmentation is inspired by "K. Klasing, D. Wollherr and M. Buss, "A clustering method for efficient segmentation of 3D laser data," 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, 2008, pp. 4043-4048, doi: 10.1109/ROBOT.2008.4543832.

  6. Thec threshhold method is from "Borges, G.A., Aldon, MJ. Line Extraction in 2D Range Images for Mobile Robotics. Journal of Intelligent and Robotic Systems 40, 267–297 (2004). https://doi.org/10.1023/B:JINT.0000038945.55712.65"

more detail:

https://blog.csdn.net/weixin_43885544/article/details/111193386

Code

1.The ground remove code references to the https://github.com/AbangLZU/plane_fit_ground_filter. And I change it to multiplane fitting.

2.The process of segmentation references to the https://github.com/FloatingObjectSegmentation/CppRBNN

Usage

mkdir build
cd build
cmake ..
make
./range forange.pcd

Result

Line compenstation

Image text

Build range image

Image text

segmentation

Image text

lidar-segmentation-based-on-range-image's People

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lidar-segmentation-based-on-range-image's Issues

不同点云文件差异较大

hi 作者:
感谢您的工作。
我把您的代码跑了一遍,但使用的1.7的pcl库。结果显示,您给出的样例点云文件跑出的结果是正常的,但使用其他的点云文件抛出的结果都不太好,不管是32线的还是64线的。请问关于这种现象有可能是什么样的原因?
另外,我查看了您的点云数据forange.pcd文件,里面的数据是常规的x/y/z/intensity,但我发现第4个数据有好多负值,比如-1,-0.009998, 这与intensity是常规的0~255之间的整值有悖,请问这里会有什么问题?

期待您的解答。

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