基于简化Mumford-Shah模型的导航基准图适配区分割方法

Mumford-Shah Model Based Critical Subset Segmentation from Navigation Reference Images

基于景象匹配的飞行器导航数字基准地图中需要划分匹配稳定的区域,以提高导航系统航迹的可靠性.该文首先讨论了描述基准图局部区域匹配稳定性的局部匹配稳健度量指标,据此定义了适配区的概念.常规的基于相关函数局部匹配稳健度量需要大量的计算时间,难以实用化.为此,文中采用了三种可快速计算的匹配稳健度量指标:相关主峰曲率、可跟踪度及特征密度,以及相应的快速算法.同时,引入了一种基于简化Mumford-Shah模型的水平集算法进行适配区划分,通过演化由模型推导出的偏微分方程,就能得到适配区和非适配区的最优划分.最后对实际的导航基准图的适配区划分试验表明,该文的适配区分割方法不仅计算速度优于基于相关函数的方法,而且可以获得合理的适配区分布.

The paper presents the segmentation critical subset method of navigation reference images for path planning of scene based vehicle guidance system. Although correlation matching method is widely used in navigation system, the traditional local robust-matching measures defined directly from correlation functions of the reference image are computationally too time-consuming. So, this paper introduces local robust-matching measures, i.e. the main peak curvature of correlation function, track ability, feature density, and corresponding fast algorithms. Then in order to segment the critical subset from the local robust-matching measure map, a new critical subset segmentation scheme is proposed based on a simplified Mumford-Shah model, and the critical and non-critical subsets are optimally differentiated by evolving the partial differential equation for Mumford-Shah model. Experiments of segmentation of critical subset from two real navigation reference images show that the computation of the proposed method is not only much faster than that based on the correlation functions, but also gives more reasonable critical areas.