Lidar Based Detector

LiDAR-based detectors are crucial for autonomous systems, aiming to accurately perceive 3D environments using light detection and ranging technology. Current research focuses on improving the robustness and generalization of these detectors, addressing challenges like sensor variations (different LiDAR models and beam counts), data scarcity, and adversarial attacks (spoofing). This involves developing novel data augmentation techniques, cross-dataset training strategies, and algorithms for data fusion (e.g., LiDAR-radar, LiDAR-camera) and point cloud denoising, often leveraging deep learning architectures. These advancements are vital for enhancing the safety and reliability of autonomous vehicles and robotics applications.