Feature-Aided RTK/LiDAR/INS Integrated Positioning System with Parallel Filters in the Ambiguity-Position-Joint Domain for Urban Environments

Abstract

As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible solution to the issue. For prior-map-free situations, as the only sensor with a global frame, RTK determines and maintains the global positioning precision of the integrated system. However, RTK performance degrades greatly in GNSS-challenged environments, and most of the existing integrated systems adopt loose coupling mode, which does nothing to improve RTK and, thus, prevents integrated systems from further improvement. Aiming at improving RTK performance in the RTK/LiDAR/INS integrated system, we proposed an innovative integrated algorithm that utilizes RTK to register LiDAR features while integrating the pre-registered LiDAR features to RTK and adopts parallel filters in the ambiguity-position-joint domain to weaken the effects of low satellite availability, cycle slips, and multipath. By doing so, we can improve the RTK fix rate and stability in GNSS-challenged environments. The results of the theoretical analyses, simulation experiments, and a road test proved that the proposed method improved RTK performance in GNSS-challenged environments and, thus, guaranteed the global positioning precision of the whole system.