Non-Iterative Localization and Fast Mapping
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This paper presents a non-iterative method for dense mapping using inertial sensor and depth camera. To obtain data correspondence, traditional methods resort to iterative algorithms which are computationally expensive. This paper proposes a novel non-iterative framework with a computationally efficient closed-form solution to the front-end of the dense mapping system. First, 3-D point clouds with 6 degrees of freedom are decoupled into independent subspaces, in which point clouds can be matched respectively. Second, without any prior knowledge, the matching process is carried out by single key-frame training in frequency domain, which reduces computational requirements dramatically and provides a closed-form solution. Third, 3-D maps are presented and fused in the subspaces directly to further reduce the complexity. In this manner, the complexity of our method is only O(nn) where n is the number of matched points. Extensive tests show that, compared with the state-of-the-arts, the proposed method is able to run at a much faster speed and yet still with comparable accuracy.
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