Tight Coupling between Radar and INS/GNSS with AUTO software for Accurate and Reliable Positioning for Autonomous Vehicles

A realized autonomous vehicle requires a ubiquitous, accurate, precise, and reliable localization system. Nowadays, the market is full of sensors that can be used for Positioning and Navigation; each with their strengths and weaknesses. Inertial Measurement Units (IMU) are often used to build Inertial Navigation Systems (INS). INS can be accurate within short durations; however, INS accumulates errors and loses its accuracy over time and distance travelled, especially when using low-cost MEMS-based sensors. An absolute position and velocity can be provided by a Global Navigation Satellite Systems (GNSS) to update the INS over time. Additionally, a barometer is used to provide absolute height information, and an odometer to provide speed updates. An integrated navigation solution consisting of IMU, GNSS-RTK, and odometer can perform well in open sky areas and on highways. Lane-level accuracy is achievable in most situations using this system, depending on sensor conditions and measurements quality. However, in downtown and urban environments, the degradation, multipath and blockage of the GNSS signals leads to poor performance for the integrated navigation system and is challenged to maintain lane level positioning. AUTO (formerly known as Coursa Drive), is a real-time integrated navigation system that provides an accurate, reliable, high rate, and continuous (always available) navigation solution for autonomous vehicles. It achieves this by integrating INS, RTK GNSS, odometer, and radar sensors with HD-Maps. AUTO performs a tight nonlinear integration of the radar and maps with the INS/GNSS/odometer system. The results presented in this paper demonstrate that radar measurements and HD-Maps can be tightly integrated with INS/GNSS in an effective manner such that the integrated system can provide a high rate, accurate, reliable, and robust navigation solution. To realize a fully autonomous vehicle, it is critical that it can operate in congested urban centers, under a wide range of conditions such as adverse weather, varying lighting conditions, and degraded GNSS signals.

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