LiDAR-aided Integrated INS/GPS Navigation System for Unmanned Ground Vehicles in Urban and Indoor Environments Using Hybrid Adaptive Scan Matching Algorithm

Shifei Liu, Mohamed M. Atia, Tashfeen B. Karamat, Aboelmagd Noureldin, Sidney Givigi

Peer Reviewed

Abstract:	This paper proposes a multi-sensor integrated navigation system for an Unmanned Ground Vehicle (UGV) that can be applied in both outdoor and indoor environments. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with Inertial Measurement Unit (IMU). Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with IMU. Moreover, the efficient yet vulnerable feature-based scan matching method, and accurate yet time-consuming scanned point-based scan matching method are combined as a hybrid scan matching algorithm. The algorithm can work and transit between two modes according to the environmental conditions. It takes advantage of the two matching methods and can achieve efficiency and robustness at the same time. In the filter design, a quaternion-based system model is derived. Real experiments are performed in both outdoor and indoor environments. Experimental results show that the multisensor integrated navigation system can implement ubiquitous navigation and sub-meter navigation accuracy can be achieved.
Published in:	Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015) September 14 - 18, 2015 Tampa Convention Center Tampa, Florida
Pages:	2311 - 2318
Cite this article:	Liu, Shifei, Atia, Mohamed M., Karamat, Tashfeen B., Noureldin, Aboelmagd, Givigi, Sidney, "LiDAR-aided Integrated INS/GPS Navigation System for Unmanned Ground Vehicles in Urban and Indoor Environments Using Hybrid Adaptive Scan Matching Algorithm," Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, Florida, September 2015, pp. 2311-2318.
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