Incremental mapping based on dot-line congruence for robot

Abstract

Based on the best congruence between dot data in the current measurement and line segments in the previously-built map,this paper proposes an incremental mapping approach for the robot in unknown environment.Each iteration of this approach consists of 3 stages:local map building,robot-pose estimating,and map integrating.A combining method of Hough transform,coincided-line detecting and least squares curve-fitting is presented and used to fit the line seg- ments from measurement in local map building.In pose estimating,the rough correspondence between the measurement and the half-baked map is obtained firstly by dot-line matching.Then removing improper match and defining weighted matrix are implemented to refine the correspondence and to reduce the errors of both measurement and map.Finally,the estimated pose is figured out by weighted least squares with the best congruence.The pseudocongruence problem in pose estimating is also discussed and solved by adding virtual lines and dots in this paper.Experimental results with real data are presented,which demonstrate that the approach is effective and robust for indoor environment mapping.