Simultaneous localization and mapping

Locating the robot while building the map#

A robot exploring an unfamiliar building cannot assume it already knows either the floor plan or its exact position. SLAM estimates both together. The Cadena et al. survey describes maps built from features, surfaces, and other representations, coupled to estimates of the robot's motion.

A humanoid could use this information to navigate between rooms. The map and estimated position provide inputs to navigation; they do not themselves choose a collision-free sequence of footsteps.

Loop closure and accumulated error#

Odometry accumulates small motion errors. Recognizing a previously visited place creates a loop-closure constraint that can correct accumulated inconsistency in a SLAM map. This distinguishes mapping with revisits from simply integrating movement over time.

Recognition can be wrong#

The survey identifies incorrect data association and perceptual aliasing as important problems. Two similar-looking corridors can be mistaken for the same place. A plausible-looking map therefore needs consistency checks and evaluation against reference measurements. Localization in an already known map is also a distinct task and does not necessarily require building a new map.