Obstacle Map

Obstacle maps are representations of environments used in robotics and autonomous navigation to depict the location and shape of obstacles, enabling safe and efficient path planning. Current research focuses on improving map creation and utilization through techniques like integrating depth and semantic information from various sensors (e.g., LiDAR, cameras), employing advanced algorithms such as diffusion models and graph neural networks for path planning and obstacle avoidance, and developing compact map representations for efficient processing. These advancements are crucial for enhancing the robustness and reliability of autonomous systems in diverse environments, ranging from indoor spaces to complex outdoor settings, and are driving progress in areas such as robot navigation, crowd navigation, and autonomous driving.