Lidar Radar Fusion

Lidar-radar fusion aims to combine the strengths of these complementary sensing technologies for improved perception in autonomous vehicles and robotics. Current research focuses on developing effective fusion architectures, including deep learning models like generative adversarial networks (GANs) and gated networks, to integrate lidar's high-resolution detail with radar's robustness in adverse weather. This work addresses challenges like data sparsity and modality differences, leading to enhanced object detection, odometry estimation, and mapping, particularly in challenging environments. The resulting improvements in perception accuracy and reliability have significant implications for safer and more robust autonomous systems.