SUMMARYA new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.
In this paper we proposed a method to implement estimation of mobile robot's position by using SURF (Speeded Up Robust Features) algorithm based on depth image in indoor environment. SURF which is derived from SIFT algorithm has the advantage of fast calculation speed and a strong robustness. The depth image is generated from a 2D LRF (Laser Range Finder) sensor which is controlled to rotate around its y-axis. According to the interest points in each frame of depth image, we find the position relationship between each two frames and make a match between the corresponding interest points. In the experiment, we will show the implementation of position estimation using the method we proposed in this paper. The accuracy and efficiency of the proposed method has also been proved in the experiment.
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