Cost Map

Cost maps are representations of an environment used in robotics and autonomous navigation to guide path planning by assigning costs to different locations based on factors like traversability, obstacles, and energy consumption. Current research focuses on learning-based approaches, employing deep neural networks (like ResNets) and inverse reinforcement learning to generate cost maps from sensor data (LiDAR, cameras, proprioceptive sensors), often incorporating object awareness and semantic understanding. This work aims to improve the robustness and efficiency of autonomous navigation in diverse environments, from indoor spaces to challenging off-road terrains, impacting fields like robotics, autonomous driving, and agricultural automation.