Absfrocr-This paper pments an algorithm to find the tine-based map that hest fits sets of two-dimensional range scan data To construct the map, we first provide an accurate means to fit a line segment to a set of uncertain paints via a maximum likelihood formalism. This scheme weights each point's inlluence on the fit according to its uncertainty, which is derived from sensor noise models. We also provide closedform formulas for the covariance of the line fit, along with methods to transform line coordinates and covariances a c m robot poses. A Chi-sqared based criterion for "knitting" together sufficiently similar lines can he used to merge lines dimtly (as we demonstrate) or as part of the framework for B line-hased SLAM implementation. Experiments using a Sick LMS-200 laser scanner and a Nomad 200 mobile robot illustrate the effectiveness of the algorithm.

This paper introduces a "weighted" matching algorithm to estimate a robot's planar displacement by matching twodimensional range scans. The influence of each scan point on the overall matching error is weighted according to its uncertainty. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. Based on models of expected sensor uncertainty, our algorithm computes the appropriate weighting for each measurement so as to optimally estimate the displacement between two consecutive poses. By explicitly modeling the various noise sources, we can also calculate the actual covariance of the displacement estimates instead of a statistical approximation of it. A realistic covariance estimate is necessary for further combining the pose displacement estimates with additional odometric and/or inertial measurements within a localization framework [1]. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range finder illustrate that the method is more accurate than prior techniques.

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