A robot driving around a room often observes an object from an awkward position, where it may be partly hidden behind furniture, too distant, or lit from behind. A conventional vision pipeline runs an object detector on whatever image is available and accepts the result even when confidence is low. A robot on wheels, unlike a fixed camera, has a further option open to it: moving to a different position and observing again. Choosing where to look next, rather than only interpreting what is already in view, is known as active perception.
A robot moving through an unfamiliar building must build a picture of its surroundings and work out where it sits within them at the same time. Solving both problems together from camera images is known as SLAM, short for simultaneous localization and mapping. The cameras used here record a normal colour image together with the distance to everything in view; such devices are called depth cameras, or RGB-D cameras, and they suit indoor robots because they provide both appearance and scale without the cost of a laser scanner. Many open-source SLAM systems exist, and they differ considerably in how they store the map, how they recognize a place already visited, how much computing power they require, and how well they cope with dim light or rapid motion.
Indoor service robots carry medication, lab samples, and supplies through hospital corridors and move goods through warehouse aisles. They share this space with people who are not trained operators and who are often not paying attention to the robot. Navigation policies for such robots are increasingly learned through reinforcement learning or imitation. When a learned policy meets a situation outside its training data, it still produces an action, and nothing in that action tells the robot or its operator that the policy may be wrong.