Abstract
This paper describes an algorithm for localization of a robot which can efficiently estimate robot in 6 degrees-of-freedom (DoF) pose which consist of position and orientation with large scale point cloud data without giving the initial pose. We introduce the Fast Scene Recognition and Alignment algorithm to reduce the computation time needed for the point cloud alignment by matching robot's scene only with the retrieved Sub-Map in database. Our developed algorithm is to extract Sub-Maps descriptor by cascading several features, and learn a Distance-Metric to increase the precision of place recognition due to the environmental changes. We then align the robot's scene with Sub-Map to estimate robot pose. Our technique has been implemented and tested extensively in different buildings. The experimental results show that our Fast Scene Recognition and Alignment system can localize mobile robot in a variety of large scale 3D point cloud dataset efficiently.
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