Occupancy Grid Mapping

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Occupancy Grid Mapping

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Occupancy Grid Mapping is a fundamental technique in robotics and autonomous systems for representing the environment as a grid of cells, where each cell contains a probability value indicating whether it is occupied, free, or unknown. This method is particularly useful in static environments where changes over time are negligible.

The core idea revolves around using Bayesian probability rules to iteratively update the occupancy state of each grid cell based on sensor measurements. Initially, each cell starts with a prior probability, often set to 0.5 (unknown). As the robot moves and collects sensor data, such as lidar or ultrasonic readings, the probability of each cell being occupied is updated using Bayes' theorem.

A key aspect of this approach is that it assumes a static world—meaning the occupancy state of cells does not change over time. The Bayesian update rule combines new sensor observations with prior knowledge, adjusting the probability in a way that reduces uncertainty. Over multiple measurements, cells gradually converge to high probability (occupied) or low probability (free), while maintaining a degree of uncertainty when evidence is insufficient.

This method is widely used in robot navigation, SLAM (Simultaneous Localization and Mapping), and environment modeling, providing a computationally efficient way to represent and reason about spatial information in a structured grid format.