An evaluation of 2D SLAM techniques based on kinect and laser scanner

Abstract

Both Laser scanner and Kinect has been widely used in robotic application for 2D Simultaneous Localization and Mapping (SLAM). The feasibility of sensors to build acquired maps are often due to the limited field of view of the sensors. In this work, we applied four methods of sensor patterns for SLAM: a single Kinect, two Kinects, a Laser scanner, a Kinect combine with a Laser scanner. For the two-sensor patterns, we proposed an efficient approach to merge the data from the both two sensors. Several SLAM algorithms (i.e. Gmapping, Hector and Crsm SLAM) were tested using the four methods to build accurate 2D maps. All the methods have been evaluated and compared in real world experiments with slight and complex features, then the performance of the three SLAM algorithms were compared particularly in the map accuracy by using the assessment algorithm of Local Grid Map Recursion Matching.