机器人视觉定位中的路口场景识别方法研究

Road Crossing Scene Recognition for Robot Visionjbased Location

针对室外巡逻机器人的视觉定位问题,提出了一种基于尺度不变特性变换(scale invariant featuretransform,SIFT)和颜色特征的路口场景识别方法。该方法首先提取路口场景图像的SIFT和颜色特征,并计算其在HSI颜色空间中的颜色直方图;然后采用K-D树和Bhattacharyya距离进行特征匹配;最终用决策公式对路口场景进行识别。为了提高SIFT算法进行场景匹配时的速度,还对场景地图库采用基于阈值分割的聚类方法进行了预处理。实验结果表明,该方法对环境光照变化、动态干扰和自身旋转有较强的鲁棒性,并能很好地识别出路口,以实现定位。

According to the problem of vision based localization for the outdoor patrol robot, an approach of road crossing scene recognition based on scale invariant feature transform（SIFT） and color features is proposed in this paper. Firstly, the SIFT features are extracted and the color histogram in HSI space is calculated. Secondly, the K-D trees algorithm is used to match SIFT features of images in road crossing images database, and the Bhattacharyya distance match resuh is calculated using color histogram. Finally, the SIFT features match result and the Bhattacharyya distance match result are combined together to confirm the suitable image in database. The image pre-classified idea is also adopted to accelerate the SIFT features matching. The experiment resuhs demonstrate that the algorithm is robust to the various illumination, dynamic disturbance and self-circumrotating, and can be used for robot location.