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Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamentaltechnique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping(SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scannermounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applyingscan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previousscans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequentialbut not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by themotion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework.Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, wepropose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid aftereach scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable ofproviding high-precision SLAM results

LASER-BASED SLAM WITH EFFICIENT OCCUPANCY LIKELIHOOD MAP LEARNING FOR DYNAMIC INDOOR SCENES