Point cloud registration plays an important role in many applications of computer vision and robotics. Outlier rejection is an essential step in this task. In this letter, we propose an efficient hand-crafted outlier ...
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Point cloud registration plays an important role in many applications of computer vision and robotics. Outlier rejection is an essential step in this task. In this letter, we propose an efficient hand-crafted outlier rejection algorithm for point cloud registration. The proposed method includes four modules: seed selection, consensus set construction and sampling, transformation matrix calculation, and hypothesis selection. Particularly, we propose a novel seed selection module based on the addition of elements in the Spatial Consistency (SC) matrix and a new noise suppression strategy in constructing consensus sets. Experimental results on various datasets show that the proposed algorithm presents superior performance over the previous hand-crafted methods.
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