Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

• Published in: Information fusion 2019-10, Vol.50, p.181-196
• Main Authors: Chiang, K.W., Tsai, G.J., Chang, H.W., Joly, C., EI-Sheimy, N.
• Format: Article
• Language: English
• Subjects: Computer Science; GNSS-denied environments; INS/GNSS; LiDAR; Mobile Mapping Systems; Robotics; Signal and Image Processing; SLAM
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2019.01.004

•The proposed method is capable of giving stable navigation and mapping solutions.•Position accuracy is around 2 m in long GNSS (more than 300 s) outage.•The mapping results achieve the meter-level accuracy.•An approximately 60% improvement of long GNSS-denied experiments is achieved. Mobile Mapping Systems (MMS) with Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) and mapping sensors have been widely developed in recent years. However current systems and results are still prone to errors, especially in GNSS-denied or multipath environments. To provide robust and stable navigation information, particularly for mapping in long-term GNSS-denied environments, we propose a semi-tightly coupled integration scheme which integrates INS/GNSS with grid-based Simultaneous Localization and Mapping (SLAM). Although traditional SLAM using LiDAR can map the GNSS-denied environment efficiently, it is only in local localization. The proposed integration scheme is based on the Extended Kalman Filter (EKF) with motion constraints. In this scheme, a measurement model for grid-based SLAM is aided by the heading and velocity information. A special innovation of this scheme is the improved fusion of GNSS/INS with the use of grid-based SLAM serves like virtual odometer and virtual compass, thus gaining reliable measurements and error models to maintain good performance during INS-only mode. In addition, the initial values for example position and heading, are given to solve global localization and loop closure problems in SLAM. Finally, a smoothing and multi-resolution map strategy are applied offline to increase the robustness and performance of the proposed grid-based SLAM. Evaluation based on experimental data shows the significant improvement by the proposed semi-tightly coupled integration scheme with low-cost INS/GNSS and LiDAR, which is able to achieve 1–2 m’ accuracy in terms of positioning and mapping. An approximately 60% improvement was achieved during long-term GNSS-denied environments using the proposed integration scheme.