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.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture