基于建筑特征及二维地图的复杂城市场景中移动机器人视觉定位算法

针对城市环境中全球定位系统(GPS)信号易受到高层建筑遮挡而无法提供准确位置信息的问题，提出了一种基于建筑物竖直侧平面特征及建筑物二维轮廓地图的移动机器人定位方法。该方法利用车载视觉，首先对两视图间的竖直直线特征进行匹配;然后基于匹配的竖直线特征对建筑物的竖直侧平面进行重建;最后，利用建筑物竖直侧平面特征及建筑物二维俯视轮廓地图，设计了一种基于随机采样一致性(RANSAC)的移动机器人视觉定位算法，从而解决了在建筑物方向任意的复杂城市环境中的机器人定位问题。实验结果表明，算法的平均定位误差约为3.6m，可以有效地提高移动机器人在复杂城市环境中自主定位的精度及鲁棒性。

Visual localization for mobile robots in complex urban scene using building features and 2D map

For the localization problem in urban areas, where Global Positioning System (GPS) cannot provide the accurate location as its signal can be easily blocked by the high-rise buildings, a visual localization method based on vertical building facades and 2D bulding boundary map was proposed. Firstly, the vertical line features across two views, which are captured with an onboard camera, were matched into pairs. Then, the vertical building facades were reconstructed using the matched vertical line pairs. Finally, a visual localization method, which utilized the reconstructed vertical building facades and 2D building boundary map, was designed under the RANSAC (RANdom Sample Consensus) framework. The proposed localization method can work in real complex urban scenes. The experimental results show that the average localization error is around 3.6m, which can effectively improve the accuracy and robustness of self-localization of mobile robots in urban environments.