Line-based SLAM with slow rotating range sensors: Results and evaluations

Abstract

This paper is concerned with the Simultaneous Localization And Mapping (SLAM) application with a mobile robot moving in a structured environment using data obtained from rotating sensors such as radars or lasers. A line-based EKF-SLAM (EKF stands for Extended Kalman Filter) algorithm is presented, which is able to deal with data that cannot be considered instantaneous when compared with the dynamics of the vehicle. When the sensor motion is fast relative to the measurement time, scans become locally distorted. A mapping solution is presented, that includes sensor motion in the observation model by taking into account the dynamics of the system. Experimental results with real-world 2D-laser scanner data are presented. Moreover a performance evaluation of the results is carried out. A quantitative performance evaluation method is proposed when dealing with a 2D line map and when a ground truth is available. It is based on the bipartite graph matching and combines several criteria that are described. A comparative study is made between the output data of the proposed method and the data processed without taking into account distortion phenomena.