<title>Using point range samples for reprojection of model views for landmark-based robot navigation</title>

Abstract

We describe a method for landmark-based robot position correction that uses a 2D image plus sparse range data to describe each landmark. Each landmark is learned by acquiring a CCD camera view from a known robot position. For each view, sparse range samples are taken with a spot laser ranging device, then a dense range estimate is created by interpolation. The choice of points to range is made based on the apparent deviation from the predicted range in each region of the landmark image. Position correction is done by using the approximate position of the robot and the interpolated range information to determine a reprojection of the stored camera view for each landmark. The reprojection is a prediction of the appearance of the landmark and its surroundings, from which an image of the landmark is extracted to use for correlation-based matching with images taken from the robot's position. Tests of the method in an industrial environment demonstrate its robustness to variation in viewpoint.