Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration

TL;DR

This paper introduces a novel evolutionary multi-task optimization algorithm for point cloud registration that improves the success ratio and robustness against local optima by using solution space cutting and a sparse-to-dense strategy.

Contribution

It proposes a new EMTO-based registration method with solution space cutting, a robust fitness function, and a cost metric, enhancing registration accuracy and success rate.

Findings

Outperforms 15 existing methods in accuracy and robustness.

Achieves higher success ratios in escaping local optima.

Demonstrates superior performance on object and scene-scale datasets.

Abstract

Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. Evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 8 evolving approaches, 4 traditional approaches and 3 deep learning approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.