  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.
