Incremental construction of a landmark-based and topological model of indoor environments by a mobile robot

This paper deals with the perception subsystem of a mobile robot which must navigate in a structured environment. We will consider the exploration task; the robot must build successive snapshot models from sensory data acquired from a laser range finder (LRF), and fuse them in a global model so that it can localize itself with respect to a pertinent reference frame. Structuration rules are required to limit the complexity of the modeling process. First of all, we only extract from the sensory data, useful landmarks which correspond to characteristic local feature groupings, like wall corners, doors, corridor crossings, ...; each landmark has its own frame and its own geometrical model. Then, correlated landmarks are kept together at an area level; finally, the global model is built from the relationships between area frames, providing the topological description of the world. For each level (landmark, area, environment), the model is represented by a random vector and a covariance matrix, updated through the use of an extended Kalman filter (EKF).