LiDAR-Based Multisensor Fusion With 3-D Digital Maps for High-Precision Positioning
Eslam Mounier, Mohamed Elhabiby, Michael J. Korenberg, Aboelmagd Noureldin
Abstract
Accurate and reliable positioning is essential for Vehicular Internet of Things (IoT) applications, such as autonomous and connected vehicles, to ensure their effective and safe operation. This calls for innovative methods that leverage various sensors and systems to fulfill such demands across diverse environmental and operational conditions. This article presents a multisensor positioning and navigation system that leverages cost-effective commercial-grade sensors for global navigation satellite system (GNSS)-challenging urban and indoor environments. The system integrates the vehicle’s onboard motion sensors (OBMSs) measurements with 3-D point clouds from light detection and ranging (LiDAR) registered to high-accuracy 3-D digital maps for sustained decimeter-level positioning accuracy. Key contributions include accurate LiDAR scan georeferencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Road test experiments on a professional land vehicle setup equipped with a multisensory navigation instrument were performed in downtown and covered parking garage environments with accurate 3-D geodatabase (GDB) available. Results from several road test trajectories demonstrate robust high-precision positioning performance with an average root mean-square error of 20 cm horizontally and 13 cm vertically, as well as position errors of less than 50 cm for 97% of the time and less than 30 cm for 90.7% of the time. The proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3-D city maps.