A probabilistic model for appearance-based robot localization

In this paper we present a method for an appearance-based modeling of the e nvironment of a mobile robot. We describe the task (localization of the rob ot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a p robability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel es timation method. We show how the parameters of the model influence the loca lization performance. We also study how many features and which features ar e needed for good localization. (C) 2001 Elsevier Science B.V. All rights r eserved.