LiDAR-GNSS Fusion to Initiate Localization at Intermediate Points on a 3D Point Cloud Map

LiDAR map-based localization and navigation play a crucial role in autonomous navigation, especially in GNSS-denied areas, by matching LiDAR data with a 3D point cloud map in real time. However, initiating localization from intermediate points on the map, distant from the origin, presents challenges. Typically, localization begins from the map’s origin, where algorithms can easily match current points to those near the origin. Challenges arise when starting from intermediate points, where the algorithm tries to match the current points’ features with those near the origin of the point cloud map. This mismatch can cause localization failure, leading to what is popularly known as the robot kidnapping problem. To tackle this challenge, we propose a solution involving the creation of unique nodes on the point cloud map by fusing LiDAR and GNSS data. Subsequently, live GNSS data is utilized to identify the nearest node, and the corresponding initial pose is published to initiate the localization problem at intermediate points on the map. Extensive real-time testing of this algorithm has been conducted at the IIT Hyderabad campus. The code for the same will be released at: https://github.com/Rakshith-Ram/Localize_Anywhere