Occupancy Grid Map

Occupancy grid maps (OGMs) represent environments as grids of cells, each assigned a probability of occupancy, providing a fundamental spatial representation for robotics and autonomous driving. Current research focuses on improving OGM accuracy and efficiency through various methods, including deep learning-based inverse sensor models, generative models for stochastic prediction, and novel algorithms that bypass computationally expensive ray-casting. These advancements are crucial for enabling real-time perception and planning in dynamic environments, with applications ranging from safer autonomous vehicles to more robust indoor robot navigation.

Papers