Occlusion Aware

Occlusion-aware research focuses on developing systems and algorithms that can robustly perceive and act in environments where objects are partially or fully hidden. Current research emphasizes improving perception through techniques like attention mechanisms and multi-modal fusion (e.g., combining lidar and camera data), and enhancing planning with methods such as graph-based search, transformer networks, and model predictive control to navigate around or reconstruct occluded objects. This work is crucial for advancing autonomous navigation, robotics, and computer vision applications, particularly in complex and dynamic environments where occlusions are common. The resulting improvements in perception and planning have significant implications for safety and efficiency in various fields.