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Micromachines 2015, 6(2), 196-215; doi:10.3390/mi6020196

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

1
College of Automation, Harbin Engineering University, 145 Nantong St., Nangang District, Harbin 150001, China
2
Department of Electrical and Computer Engineering, Royal Military College of Canada, P.O. Box 17000, Station Forces, Kingston, ON K7K 7B4, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy and Joost Lötters
Received: 24 November 2014 / Accepted: 20 January 2015 / Published: 28 January 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
View Full-Text   |   Download PDF [1145 KB, uploaded 28 January 2015]   |  

Abstract

The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy. View Full-Text
Keywords: LiDAR; MEMS-based INS; UGV; indoor navigation; covariance estimation; multi-sensor integration LiDAR; MEMS-based INS; UGV; indoor navigation; covariance estimation; multi-sensor integration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, S.; Atia, M.M.; Gao, Y.; Noureldin, A. Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems. Micromachines 2015, 6, 196-215.

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