VODRAC: Efficient and robust correspondence-based point cloud registration with extreme outlier ratios

Abstract

Point set registration (PSR) from correspondences is a basic problem in the area of computer vision, robotics and remote sensing. Nevertheless, because of the limited accuracy of current correspondence building and feature matching technologies, PSR is frequently afflicted by the problem of outliers. In this work, we put forward VODRAC (VOting-based Double-point RAndom sampling with Compatibility weighting), a fast, highly robust and practically effective solution for the PSR problem as well as its real-world applications. To realize this, our first contribution is to integrate the scale-invariant constraint with a double-point random sampling framework to achieve the rapid seeking of inlier candidates and the rough rejection of outliers in the meantime. The second contribution is that we introduce the concept of weight matrix for PSR which is to use the robust loss function for weight computation between pairwise correspondences and also propose a time-efficient way to embed the construction of this matrix into the operation of the algorithm without requiring additional computational time. Moreover, we employ the correspondence voting technique to accelerate the consensus maximization (convergence) of the algorithm, as our third contribution. Through comprehensive experiments over multiple actual datasets, we demonstrate that VODRAC is fast, accurate, and highly robust against up to 99% of outliers (e.g. solving PSR within 2 s when there exist 99% outliers among 1000 correspondences), outperforming existing state-of-the-art (SOTA) robust algorithms.

Keywords

• Point set registration
• Robust estimation
• Consensus maximization
• Object localization
• Scan matching