针对飞行机器人巡检的海量电力杆塔图像,以电力杆塔和架空电力线路巡检飞行机器人间的相对位置关系为先验信息,估计电力杆塔在飞行机器人巡检拍摄得到图像中的位置范围.然后利用这个位置范围作为可变型部件模型(deformable model part,DPM)检测区域的约束,从而精确快速定位电力杆塔的位置.实验结果表明:通过飞行机器人巡检获取的视频图像进行测试,验证了融合地理位置信息后的可变型部件模型的方法(GI-DPM)的有效性,提高了电力杆塔的检测速度.
For traditional loop closure detection algorithm,only using the vectorization of point features to build visual dictionary is likely to cause perceptual ambiguity.In addition,when scene lacks texture information,the number of point features extracted from it will be small and cannot describe the image effectively.Therefore,this paper proposes a loop closure detection algorithm which combines point and line features.To better recognize scenes with hybrid features,the building process of traditional dictionary tree is improved in the paper.The features with different flag bits were clustered separately to construct a mixed dictionary tree and word vectors that can represent the hybrid features,which can better describe structure and texture information of scene.To ensure that the similarity score between images is more reasonable,different similarity coefficients were set in different scenes,and the candidate frame with the highest similarity score was selected as the candidate closed loop.Experiments show that the point line comprehensive feature was superior to the single feature in the structured scene and the strong texture scene,the recall rate of the proposed algorithm was higher than the state of the art methods when the accuracy is 100%,and the algorithm can be applied to more diverse environments.
