基于视觉同时定位与地图构建数据关联优化算法

数据关联的复杂程度随着地图规模的不断扩大而增加是导致机器人同时定位与地图创建（SLAM）实时性差的一个主要原因。在SLAM系统中，主要应用尺度不变特征变换（SIFT）算法提取自然路标。提出两种方法来改进数据关联的实时性：1）提取感兴趣区域；2）引入当前路标的物理位置信息作预判断。实验结果表明，所提的改进方法是可靠的，改善算法复杂度的效果是显而易见的。

Optimized data association algorithm based on visual simultaneous localization and mapping

The scale of data association increases as the map grows, which is one of the major reasons for the poor real-time performance of robot in the process of Simultaneous Localization And Mapping (SLAM). In visual SLAM system, SIFT (Scale Invariant Feature Transform) algorithm was used to extract the natural landmarks. Two improvements were introduced to improve the real-time of data association：firstly，extracted the "interest region"； secondly，took into account the physical location of current landmarks. The experimental results indicate that this kind of improvement method is reliable, and the capability of reducing computational complexity is obvious.