Probabilistic Estimation Mechanisms And Tesselated Representations For Sensor Fusion

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Abstract
Two fundamental issues in sensor fusion are (1) the definition of model spaces for representing objects of interest and (2) the definition of estimation procedures for instantiating repre-sentations, with descriptions of uncertainty, from noisy observa-tions. In 3-D perception, model spaces frequently are defined by contour and surface descriptions, such as line segments and planar patches. These models impose strong geometric limitations on the class of scenes that can be modelled and involve segmentation decisions that make model updating difficult. In this paper, we show that random field models provide attractive, alternative representations for the problem of creating spatial descriptions from stereo and sonar range measurements. For stereo ranging, we model the depth at every pixel in the image as a random variable. Maximum likelihood or Bayesian formulations of the matching problem allow us to express the uncertainty in depth at each pixel that results from matching in noisy images. For sonar ranging, we describe a tesselated spatial representation that encodes spatial occupancy probability at each cell. We derive a probabilistic scheme for updating estimates of spatial occupancy from a model of uncertainty in sonar range measurements. These representations can be used in conjunction to build occupancy maps from both sonar and stereo range measurements. We show preliminary results from sonar and single-scanline stereo that illustrate the potential of this ap-proach. We conclude with a discussion of the advantages of the representations and estimation procedures used in this paper over approaches based on contour and surface models.

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