Weighted line fitting algorithms for mobile robot map building and efficient data representation

This paper presents an algorithm to find the line-based map that best fits sets of two-dimensional range scan data that are acquired from multiple poses. To construct these maps, we first provide an accurate means to fit a line segment to a set of uncertain points via a maximum likelihood formalism. This scheme weights each point’s influence on the fit according to its uncertainty, which is derived from sensor noise models. We also provide closed-form formulas for the covariance of the line fit. The basic line fitting procedure is then used to “knit” together lines from multiple robot poses, taking into account the uncertainty in the robot’s position. Experiments using a Sick LMS-200 laser scanner and a Nomad 200 mobile robot illustrate the method.