What is semantic segmentation?

Semantic segmentation is the capability of an autonomous mobile robot to analyze its environment by detecting and classifying every object within its field of view. Unlike simple object detection, semantic segmentation provides detailed understanding by assigning a class label to each pixel in an image—allowing the robot to recognize what different objects are (e.g., walls, boxes, people) and distinguish between them.

This semantic understanding enhances the robot’s ability to build accurate maps and models of its surroundings, improving navigation and operational safety. For example, a wall is recognized as a static, permanent obstacle, while a box or person is identified as movable, which helps the robot make better decisions when planning paths or avoiding collisions.

Semantic segmentation is an advanced vision feature requiring high-quality camera inputs and machine learning models that have been trained to recognize various classes of objects. It plays a crucial role in enabling vision-based autonomous mobile robots to operate efficiently and safely in dynamic, unstructured environments where human presence and moving equipment coexist.