Occupancy Map

Occupancy maps represent the presence or absence of obstacles in an environment, crucial for robot navigation and autonomous driving. Current research focuses on improving the accuracy, efficiency, and resolution of these maps, employing techniques like neural radiance fields, diffusion models, and Gaussian mixture models to create both 2D and 3D representations, often integrating data from multiple sensors (LiDAR, cameras, IMU). These advancements are driven by the need for robust and computationally efficient methods for real-time mapping in dynamic environments, impacting fields like robotics, autonomous vehicles, and augmented reality. Improved occupancy map generation leads to safer and more efficient navigation systems.