Abstract
Abstract—A new algorithm for a three-dimensional reconstruction of the surrounding environment is proposed. The algorithm is able to create accurate three-dimensional maps in real-time with the help of a RGB-D depth camera. This algorithm can be used in autonomous mobile robotics, where the robot needs to localize itself in unknown environments by processing onboard sensors without external reference systems such as a global positioning system. We analyze various combinations of common detectors and descriptors of visual features in terms of their recognition efficiency. To match the point clouds between consecutive frames, the iterative closest point (ICP) method is used. To improve the quality of the three-dimensional reconstruction, an adaptive approach to calculating the estimate of the camera position is suggested. The proposed system is able to efficiently process complex scenes at a high rate, and its performance on available benchmark databases is comparable with that of the state-of-the-art systems.
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