Simultaneous Localization and Mapping (SLAM) has revolutionized robotics and autonomous systems, enabling precise navigation and environmental mapping. While traditional SLAM systems rely on cameras and LiDAR, their performance often falters under challenging weather conditions such as heavy fog, rain, or snow. Radar SLAM emerges as a robust alternative, leveraging the radar’s resilience to environmental changes and its unique sensing capabilities. tract

This webinar delves into the principles and advancements of radar-only SLAM, highlighting its ability to achieve reliable localization and mapping even in extreme weather scenarios. We will explore cutting-edge technologies from Cadence Tensilica, including novel and accurate Iterative Closest Point (ICP) algorithms for pose estimation implemented on Cadence Vision DSP and AI products, providing real time radar SLAM application optimized for high performance. We will also examine how higher accuracy is obtained even with relatively sparse radar data.

Join us to uncover the promising potential of Radar SLAM in enabling autonomous navigation and mapping and to discuss its future applications in robotics, transportation, and beyond.