Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization

Abstract

State-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This approach is independent of the type of feature descriptors and SLAM used onboard the AR/MR device. The mesh could be reduced to the point cloud that only takes vertices. We analyzed and compared different point cloud registration methods applicable to the problem. In addition, we proposed a new pipeline Feature Inliers Graph Registration Approach (FIGRA) for the co-localization of AR/MR devices using point clouds. The comparative analysis of Go-ICP, Bayesian-ICP, FGR, Teaser++, and FIGRA shows that feature-based methods are more robust and faster than ICP-based methods. Through an in-depth comparison of the feature-based methods with the usual fast point feature histogram and the new weighted height image descriptor, we found that FIGRA has a better performance due to its effective graph-theoretic base. The proposed pipeline allows one to match point clouds in complex real scenarios with low overlap and sparse point density.