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Mobile Information Systems
Volume 2017, Article ID 4802159, 11 pages
Research Article

An Online Solution of LiDAR Scan Matching Aided Inertial Navigation System for Indoor Mobile Mapping

GNSS Research Centre, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

Correspondence should be addressed to Jian Tang; nc.ude.uhw@naijgnat

Received 23 February 2017; Revised 17 May 2017; Accepted 30 May 2017; Published 3 July 2017

Academic Editor: Gonzalo Seco-Granados

Copyright © 2017 Xiaoji Niu et al. 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.


Multisensors (LiDAR/IMU/CAMERA) integrated Simultaneous Location and Mapping (SLAM) technology for navigation and mobile mapping in a GNSS-denied environment, such as indoor areas, dense forests, or urban canyons, becomes a promising solution. An online (real-time) version of such system can extremely extend its applications, especially for indoor mobile mapping. However, the real-time response issue of multisensors is a big challenge for an online SLAM system, due to the different sampling frequencies and processing time of different algorithms. In this paper, an online Extended Kalman Filter (EKF) integrated algorithm of LiDAR scan matching and IMU mechanization for Unmanned Ground Vehicle (UGV) indoor navigation system is introduced. Since LiDAR scan matching is considerably more time consuming than the IMU mechanism, the real-time synchronous issue is solved via a one-step-error-state-transition method in EKF. Stationary and dynamic field tests had been performed using a UGV platform along typical corridor of office building. Compared to the traditional sequential postprocessed EKF algorithm, the proposed method can significantly mitigate the time delay of navigation outputs under the premise of guaranteeing the positioning accuracy, which can be used as an online navigation solution for indoor mobile mapping.