This fork includes
-
a build system for the c++ code (cmake)
-
an example that reads poses from a textfile, and pointcloud of your choosing, and visualizes the results
- Dependencies
- Eigen3
- PCL
- cmake
git clone <this repo> && cd <this repo> mkdir build && cd build make
usage: ./ndd_demo <poses.txt> <pcl_dir>
Poses are expected to contain at least x, y, z coordinates in a txt or csv file
Keyframes are expected to be .pcd files
The code for our paper accepted by IROS 2022 (NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection)
We present a global descriptor, named Normal Distribution Descriptor (NDD), for 3D point cloud loop closure detection. The descriptor encodes both the probability density score and entropy of a point cloud as the descriptor. We also propose a fast rotation alignment process and use correlation coefficient as the similarity between descriptors.
https://arxiv.org/abs/2209.12513
@INPROCEEDINGS { IROS-2022-Zhou,
author = {Ruihao Zhou, Li He, Hong Zhang, Xubin Lin, Yisheng Guan},
title = { NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection },
booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
year = { 2022 },
month = { Oct. },
address = { Japan }
}
To run the demo, simply run NDD_demo.m.
Demo video can be found at: https://youtu.be/OYfp4tURhr0