title: INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm.
abstract: This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory.
authors.affiliation: College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin 150001, China. gaoyanbin@hrbeu.edu.cn.
authors.forename: Yanbin
authors.initials: Y
authors.lastname: Gao
authors.affiliation: College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin 150001, China. shifeiliu@gmail.com.
authors.forename: Shifei
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Electrical and Computer Engineering, Royal Military College of Canada, P.O. Box 17000, Station Forces, Kingston, ON K7K 7B4, Canada. mohamed.maher.atia@gmail.com.
authors.forename: Mohamed M
authors.initials: MM
authors.lastname: Atia
authors.affiliation: Department of Electrical and Computer Engineering, Royal Military College of Canada, P.O. Box 17000, Station Forces, Kingston, ON K7K 7B4, Canada. aboelmagd.noureldin@rmc.ca.
authors.forename: Aboelmagd
authors.initials: A
authors.lastname: Noureldin
keywords: LiDAR; Scan Matching; Unmanned Ground Vehicle; Urban and Indoor Navigation