外文文献及翻译：基于视觉的矿井救援机器人场景识别scenerecognitionforminerescuerobotlocalizationbasedonvision

外文文献及翻译：基于视觉的矿井救援机器人场景识别scenerecognitionforminerescuerobotlocalizationbasedonvision内容摘要：

description of the place where the robot locates. In our system EVID70 camera has a view field of 177。 170176。 . Considering the overlap effect, we sample environment every 45176。 to get 8 images. Let the 8 images as hidden state Si (1≤i≤8), the created HMM can be illustrated by . The parameters of HMM, aij and bjk, are achieved by learning, using BaulmWelch algorithm[14]. The threshold of convergence is set as . As for the edge of topological map, we assign it with distance information between two vertices. The distances can be puted according to odometry readings. HMM of environment To locate itself on the topological map, robot must run its „eye‟ on environment and extract a landmark sequence L1′ − Lk′ , then search the map for the best matched vertex (scene). Different from traditional probabilistic localization[15], in our system localization problem can be converted to the evaluation problem of HMM. The vertex with the greatest evaluation value, which must also be greater than a threshold, is taken as the best matched vertex, which indicates the most possible place where the robot is. 4 Match strategy based on fuzzy logic One of the key issues in image match problem is to choose the most effective features or descriptors to represent the original image. Due to robot movement, those extracted landmark regions will change at pixel level. So, the descriptors or features chosen should be invariant to some extent according to the changes of scale, rotation and viewpoint etc. In this paper, we use 4 features monly adopted in the munity that are briefly described as follows. GO: Gradient orientation. It has been proved that illumination and rotation changes are likely to have less influence on it[5]. ASM and ENT: Angular second moment and entropy, which are two texture descriptors. H: Hue, which is used to describe the fundamental information of the image. Another key issue in match problem is to choose a good match strategy or algorithm. Usually nearest neighbor strategy (NN) is used to measure the similarity between two patterns. But we have found in the experiments that NN can‟t adequately exhibit the individual descriptor or feature‟s contribution to similarity measurement. As indicated in , the input image (a) es from different view of (b). But the distance between (a) and (b) puted by Jefferey divergence is larger than (c). To solve the problem, we design a new match algorithm based on fuzzy logic for exhibiting the subtle changes of each features. The algorithm is described as below. And the landmark in the database whose fused similarity degree is higher than any others is taken as the best match. The match results of (b) and (c) are demonstrated by . As indicated, this method can measure the similarity effectively between two patterns. Similarity puted using fuzzy strategy 5 Experiments and analysis The localization system has been implemented on a mobile robot, which is built by our laboratory. The vision system is posed of a CCD camera and a framegrabber IVC4200. The resolution of image is set to be 400320 and the sample frequency is set to be 10 frames/s. The puter system is posed of 1 GHz processor and 512 M memory, which is carried by the robot. Presently the robot works in indoor environments. Because HMM is adopted to represent and recognize the scene, our system has the ability to capture the discrimination about distribution。