Radar Simultaneous Localization and Mapping

Radar Simultaneous Localization and Mapping (SLAM) aims to build maps of an environment while simultaneously tracking a robot's location using radar sensors, offering robustness in challenging conditions where vision fails. Current research focuses on improving accuracy and efficiency through advanced algorithms like Normal Distributions Transform (NDT) variations, incorporating radar intensity and Doppler information, and robustly handling uncertainty in radar measurements, including the development of novel odometry techniques. These advancements are significant for applications in robotics, particularly in autonomous navigation for scenarios like search and rescue or autonomous driving, where reliable localization and mapping are crucial even in adverse conditions.