Decoupling odometry and exteroceptive perception in building a global world map of a mobile robot: the use of local maps

Abstract

A mobile robot navigates in an environment and incrementally builds a map while locating itself. The quality of the map depends on the sensor models. Due to the difficulty of defining a stochastic odometric model, the classical approach based on Kalman filtering is revisited in order to decouple the information provided by the odometric sensor and exteroceptive sensors. The proposed approach is based on the observation and fusion of geometric relationships between objects. These relationships are represented in local maps and related in a global map where odometric information is exploited. The resulting local maps are invariant with respect to biases introduced in the robot location (position and orientation) due to sliding for instance. Experimental results on real data compare both the classical and the proposed approach.