Radar Odometry

Radar odometry aims to estimate the movement of a vehicle or robot using radar sensor data, enabling robust localization even in challenging weather conditions where vision-based systems fail. Current research focuses on improving accuracy and efficiency through advanced point cloud processing techniques, incorporating Doppler velocity information for more precise ego-motion estimation, and developing novel algorithms like those based on scan matching, Kalman filtering, and deep learning for improved robustness to noise and outliers. These advancements are crucial for enhancing the reliability and performance of autonomous navigation systems in various applications, including autonomous driving and robotics.