Real-Time 2-D Lidar Odometry Based on ICP

This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multi...

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Autores principales:	Fuxing Li, Shenglan Liu, Xuedong Zhao, Liyan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	2-D lidar multi-scale feature extraction motion estimation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/db86df0c7c4c4660813b14067b95b6c3
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id	oai:doaj.org-article:db86df0c7c4c4660813b14067b95b6c3
record_format	dspace
spelling	oai:doaj.org-article:db86df0c7c4c4660813b14067b95b6c32021-11-11T19:10:18ZReal-Time 2-D Lidar Odometry Based on ICP10.3390/s212171621424-8220https://doaj.org/article/db86df0c7c4c4660813b14067b95b6c32021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7162https://doaj.org/toc/1424-8220This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.Fuxing LiShenglan LiuXuedong ZhaoLiyan ZhangMDPI AGarticle2-D lidarmulti-scalefeature extractionmotion estimationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7162, p 7162 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	2-D lidar multi-scale feature extraction motion estimation Chemical technology TP1-1185
spellingShingle	2-D lidar multi-scale feature extraction motion estimation Chemical technology TP1-1185 Fuxing Li Shenglan Liu Xuedong Zhao Liyan Zhang Real-Time 2-D Lidar Odometry Based on ICP
description	This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.
format	article
author	Fuxing Li Shenglan Liu Xuedong Zhao Liyan Zhang
author_facet	Fuxing Li Shenglan Liu Xuedong Zhao Liyan Zhang
author_sort	Fuxing Li
title	Real-Time 2-D Lidar Odometry Based on ICP
title_short	Real-Time 2-D Lidar Odometry Based on ICP
title_full	Real-Time 2-D Lidar Odometry Based on ICP
title_fullStr	Real-Time 2-D Lidar Odometry Based on ICP
title_full_unstemmed	Real-Time 2-D Lidar Odometry Based on ICP
title_sort	real-time 2-d lidar odometry based on icp
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/db86df0c7c4c4660813b14067b95b6c3
work_keys_str_mv	AT fuxingli realtime2dlidarodometrybasedonicp AT shenglanliu realtime2dlidarodometrybasedonicp AT xuedongzhao realtime2dlidarodometrybasedonicp AT liyanzhang realtime2dlidarodometrybasedonicp
_version_	1718431614118133760

Real-Time 2-D Lidar Odometry Based on ICP

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