Self Localization

Self-localization, the ability of a system (robot, vehicle, etc.) to determine its position and orientation, is crucial for autonomous navigation and various applications. Current research emphasizes robust and accurate localization using diverse sensor modalities, including LiDAR, radar, and cameras, often employing techniques like Monte Carlo Localization (MCL), Iterative Closest Point (ICP) algorithms, and extended object tracking. These methods are being enhanced through cooperative localization strategies, leveraging infrastructure (e.g., roadside units) and data-driven approaches like neural networks for improved accuracy and resilience in challenging environments. Advances in self-localization are driving progress in autonomous driving, robotics, and intelligent transportation systems.