Examining the Benefits of LiDAR Odometry Integrated with GNSS and INS in Urban Areas

Abstract

Navigation solutions have become a vital part of our lives and a precise positioning solution is required everywhere. The global navigation satellite system (GNSS) is the most commonly used technology in navigation and positioning along with the Inertial Navigation System (INS). Generally, the low-cost GNSS receivers provide a reliable positioning solution with meter level accuracy when there is a direct line of sight between the receiver’s antenna and at least four satellites. However, this accuracy is degraded in challenging environments such as in urban areas where the solution provided by the GNSS is degraded due to multipath, poor satellite geometry and possibly complete satellite signal blockage. Therefore, to overcome the aforementioned limitations, integration with a coupling of inertial sensors and possibly an odometer is usually employed. To formulate such navigation solution from both, the odometer and inertial sensors, the three-dimensional reduced inertial sensor system (3D-RISS) algorithm has been developed to integrate with GNSS using an optimal estimation technique such as a Kalman filter. Despite the latter integration, in areas such as in urban areas and downtown cores where GNSS receivers may in incur prolonged outages, the solution relying on inertial technology is prone to rapid drift resulting in very large position errors. In fact, this has been the main reason why other sensors such as Radars, cameras, and LiDARs have been explored in the past few years to provide more reliable position updates through challenging GNSS environments. This paper focuses on exploring the benefits of using LiDAR to be integrated with the 3D-RISS in order to bridge GNSS outages in challenging urban environment by showing the performance on real road test experiments that were conducted in downtown Toronto.