Robust 2D Point set matching with kernel mean p- power error loss

In this paper, we propose a novel point set matching algorithm to improve the matching precision in the presence of non-Gaussian noises and outliers. In our method, a non-second order similarity measure known as Kernel Mean p- Power Error (KMPE) loss is employed as the matching cost function. We introduce a local optimal solution for computing the rigid transform by repeating the correspondence estimation and parameter updating processes. This new algorithm assigns a non-linear distance evaluation in kernel space according to the current estimation of the correspondence to yield a more accurate matching result between two point sets in practice. Experimental results demonstrate that our algorithm is more robust and accurate than the traditional ICP and the state-ofthe- art algorithms.