Efficient and Distributed Large-Scale Point Cloud Bundle Adjustment via Majorization-Minimization

TL;DR

This paper introduces BALM3.0, a distributed bundle adjustment method for large-scale point clouds that significantly improves computational efficiency and reduces memory usage by decoupling scan poses using a majorization-minimization algorithm.

Contribution

The paper proposes a novel decoupling approach for large-scale point cloud bundle adjustment using majorization-minimization, enabling distributed processing and linear time complexity.

Findings

Achieves up to 704 times faster optimization speed

Reduces memory usage to one-eighth of traditional methods

Successfully optimizes large-scale data with 21,436 poses

Abstract

Point cloud bundle adjustment is critical in large-scale point cloud mapping. However, it is both computationally and memory intensive, with its complexity growing cubically as the number of scan poses increases. This paper presents BALM3.0, an efficient and distributed large-scale point cloud bundle adjustment method. The proposed method employs the majorization-minimization algorithm to decouple the scan poses in the bundle adjustment process, thus performing the point cloud bundle adjustment on large-scale data with improved computational efficiency. The key difficulty of applying majorization-minimization on bundle adjustment is to identify the proper surrogate cost function. In this paper, the proposed surrogate cost function is based on the point-to-plane distance. The primary advantages of decoupling the scan poses via a majorization-minimization algorithm stem from two key aspects. First, the decoupling of scan poses reduces the optimization time complexity from cubic to linear, significantly enhancing the computational efficiency of the bundle adjustment process in large-scale environments. Second, it lays the theoretical foundation for distributed bundle adjustment. By distributing both data and computation across multiple devices, this approach helps overcome the limitations posed by large memory and computational requirements, which may be difficult for a single device to handle. The proposed method is extensively evaluated in both simulated and real-world environments. The results demonstrate that the proposed method achieves the same optimal residual with comparable accuracy while offering up to 704 times faster optimization speed and reducing memory usage to 1/8. Furthermore, this paper also presented and implemented a distributed bundle adjustment framework and successfully optimized large-scale data (21,436 poses with 70 GB point clouds) with four consumer-level laptops.