This work presents a new recursive robust filtering approach for feature-based 3D registration. Unlike the common state-of-the-art alignment algorithms, the proposed method has four advantages that have not yet occurred altogether in any previous solution. For instance, it is able to deal with inherent noise contaminating sensory data; it is robust to uncertainties caused by noisy feature localisation; it also combines the advantages of both (Formula presented.) and (Formula presented.) norms for a higher performance and a more prospective prevention of local minima. The result is an accurate and stable rigid body transformation. The latter enables a thorough control over the convergence regarding the alignment as well as a correct assessment of the quality of registration. The mathematical rationale behind the proposed approach is explained, and the results are validated on physical and synthetic data.

翻译：这项工作为基于地貌的 3D 登记提供了一种新的循环式稳健过滤方法。与通用的最新校准算法不同,拟议方法具有四个尚未在以前任何解决办法中完全出现的优势。例如,它能够处理内在噪音污染感官数据的问题;它对于噪音特性定位造成的不确定性是稳健的;它还结合了两种方法的优势:提高性能和对当地微型进行更具有前景的预防的规范(Formula.)和(Formula.);结果是精确和稳定的僵硬体变形;后者能够彻底控制关于校准的趋同以及对登记质量的正确评估;解释了拟议方法背后的数学原理,并对物理和合成数据进行了验证。