The problem of building statistical models for multi-sensor perception in unstructured outdoor environments is addressed in this book. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and to fuse sensory data are investigated. This book presents techniques for combining non- linear dimensionality reduction with parametric learning through Expectation Maximisation to build general and compact representations of natural features. The robustness of localisation and mapping algorithms is directly related to reliable data association. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision.

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