On Bundle Adjustment for Multiview PointCloud Registration

TL;DR

This paper introduces a novel bundle adjustment method for multiview point-cloud registration that improves accuracy and efficiency, achieving centimeter-level positioning errors in real-world scenarios.

Contribution

It proposes a new objective function considering noise and computational cost, with a voxel-based registration system for practical applications.

Findings

Outperforms baselines in accuracy and speed

Achieves centimeter-level positioning errors

Uses voxel-based quantization for real-world applicability

Abstract

Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global consistency. So in this work, we make the multiview point-cloud registration more tractable from a different perspective in resolving range-based BA. Based on this analysis, we propose an objective function that takes both measurement noises and computational cost into account. For the feature parameter update, instead of calculating the global distribution parameters from the raw measurements, we aggregate the local distributions upon the pose update at each iteration. The computational cost of feature update is then only dependent on the number of scans. Finally, we develop a multiview registration system using voxel-based quantization that can be applied in real-world scenarios. The experimental results demonstrate our superiority over the baselines in terms of both accuracy and speed. Moreover, the results also show that our average positioning errors achieve the centimeter level.