Abstract
Thispaper presents a method of simultaneous localization and mapping (SLAM) in indoor environment using extended Kalman filter (EKF) with the straight line segments as the adopted geometrical feature. By using conventional two dimensional laser range finder (LRF) as the main sensor, robot finds a number of points scanned from the surrounding environment.Split-and-Merge is one of the mostpopular algorithms to extract straight line segments from these raw scanned points. However, during the splitting procedure, especially when long range LRF is employed, the widely adoptedmethod named iterative end point fitting (IEPF) has difficulty to segment the points' cluster into collinear subsets correctly and sufficiently due to its constant splitting criterion.To solve this problem,we introduce modified iterative end point fitting (MIEPF) which calculates the splitting criterion for each cluster individually.Simulation and experimental results show the effectiveness of the proposed algorithm.
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