VorSLAM算法中基于多规则的数据关联方法

针对单独依据马氏距离(Mahalanobis distance)的数据关联(Data association, DA)算法不能保证输出正确结果的问题, 结合VorSLAM (Voronoi partition based SLAM)算法所采用的混合地图表示方法的特点, 本文提出了一个基于多规则的数据关联方法. 该数据关联方法依据的规则包括局部搜索规则、传感器观测特征的单向性规则、 马氏距离规则和轮廓匹配规则,诸个规则在每个数据关联周期依次执行. 局部搜索规则和传感器观测特征的单向性规则可以有效地降低数据关联的搜索空间,同时可避免一类潜在的数据关联错误; 马氏距离利用了特征参数表示的特征位置信息寻找多个可能的数据关联假设; 根据VorSLAM算法中局部地图描述了产生对应特征的局部环境轮廓信息, 轮廓匹配规则从多个可能的数据关联假设中识别出正确的数据关联假设. 基于多规则的数据关联方法系统可靠地解决了VorSLAM算法中的数据关联问题, 方法的有效性通过两个室内环境的实验得到了验证.

A Data Association Approach Based on Multi-rules in VorSLAM

To solve the problem that the data association (DA) algorithm based only on Mahalanobis distance cannot ensure to produce correct results, this paper proposes a data association approach based on multi-rules, which has utilized the characteristic of the hybrid metric map representation, adopted in VorSLAM. This approach consists of the rule of local searching, the rule of unidirectivity existing in the corner's observation, the rule of Mahalanobis distance and the rule of shape matching. These rules execute in turn in each data association circle. The former two rules decrease the searching space of the data association and avoid a sort of wrong data association. The rule of Mahalanobis distance can produce a set of potential data association hypothese according to the location information expressed in the feature's parameters. Utilizing the characteristic that in VorSLAM each local map describes the local environment's contour of its corresponding feature, the rule of shape matching finally recognizes the correct data association hypothesis. The data association based on multi-rules has systematically and reliably solved the data association problem in VorSLAM. The efficiency of the proposed approach is verified in two experiments carried out in indoor environments.