Abstract
Images with large-area repetitive texture, significant viewpoint, and illumination changes as well as occlusions often induce high-percentage keypoint mismatches, affecting the performance of vision-based mapping and navigation. Traditional methods for mismatch elimination tend to fail when the percentage of mismatches is high. In order to remove mismatches effectively, a new geometry-based approach is proposed in this paper, where Geometric Correspondence Feature (<small>GCF</small>) is used to represent the tentative correspondence. Based on the clustering property of <small>GCFs</small> from correct matches, a new clustering algorithm is developed to identify the cluster formed by the correct matches. With the defined quality factor calculated from the identified cluster, a Progressive Sample Consensus (<small>PROSAC</small>) process integrated with hyperplane-model is employed to further eliminate mismatches. Extensive experiments based on both simulated and real images in indoor and outdoor environments have demonstrated that the proposed approach can significantly improve the performance of mismatch elimination in the presence of high-percentage mismatches.
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