<title>Robot self-location based on corner detection</title>

Abstract

This paper presents a decision theoretic method of establishing the position of a mobile robot in a known environment. Boundaries of regions of varying light intensities may be extracted from visual data gathered by the rotation of a camera about the robot position. Some of these boundaries will represent corners of the region. The identification of these corners may further be enhanced using range data. The methods in this paper rely on the probability of viewing corners and on the probability density functions of the measured view angle of corners or the separation angle between corners. View angles are used when compass knowledge is available otherwise corner separation angles are used. The probability density functions of these corner angles are derived from the region geometry and prior knowledge (if any) of the robot position. The known environment is decomposed into visibility regions for sets of corners. The probability of viewing a set of corners depends on the likelihood of the robot being positioned in one of these visibility regions. An optimal decision procedure is established to identify the corner set (or visibility region) based on visual data from a circular scan about the robot position. With this information a least squares estimate of the robot position is derived.