Real-Time Precision Navigation Design for Autonomous Vehicle with EGI System

Abstract. This research presents an innovative system framework, and a multi-sensor integration algorithm aimed at improving the navigation accuracy of autonomous vehicle (AV). This study addresses the shortcomings of the LiDAR-centric approach used in Autoware, a popular open-source platform for self-driving cars. This article presents FalcoNav.AI, a novel INS-centric method, integrating inertial navigation, satellite positioning, Light Detection and Ranging (LiDAR), and High-Definition (HD) maps to enhance navigational performance. An Extended Kalman Filter (EKF) is employed for efficient data fusion and processing of Inertial Navigation System (INS). The performance of system is evaluated in various environments, including open sky, Global Navigation Satellite System (GNSS) challenge and GNSS denied areas, showing significant improvements in navigation accuracy and reliability. The key components of the system include affordable Velodyne VLP-16 and a custom-built Embedded GNSS/INS (EGI) Inertial Navigation System (EGI-370). Experiments demonstrate that the system achieves "Where-in-Lane" level accuracy, highlighting its potential for wide application in autonomous vehicle. This innovation represents a significant advance towards more dependable and precise navigation in a wide range of driving conditions.