Uncertain map making in natural environments

Building on previous work on incremental natural scene modelling for mobile robot navigation, we focus in this paper on the problem of representing and managing uncertainties. The environment is composed of ground regions and objects. Objects (e.g., rocks) are represented by an uncertain state vector (location) and a variance-covariance matrix. Their shapes are approximated by ellipsoids. Landmarks are defined as objects with specific properties (discrimination, accuracy) that permit to use them for robot localization and for anchoring the environment model. Model updating is based on an extended Kalman filter. Experimental results are given that show the construction of a consistent model over tens of meters