外文文獻(xiàn)及翻譯基于視覺的礦井救援機(jī)器人場(chǎng)景識(shí)別

1、中文 中文 3100 字出處： 出處：Transactions of Nonferrous Metals Society of China, 2008, 18(2): 432-437附錄 附錄 英文原文 英文原文Scene recognition for mine rescue robot localization based on visionAbstract：A new scene recognition system was p

2、resented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environ

3、ment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landmarks are organized by using HMM to represent the

4、 scene where the robot is, and fuzzy logic strategy is used to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluati

5、on problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of re

6、cognition and localization in both static and dynamic mine environments.Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov modelfeature for scene recognition. In ex

7、periments, the NN is not good enough to express the similarity between two patterns. Furthermore, the selected features can not represent the scene thoroughly according to the state-of-art pattern recognition, which make

8、s recognition not reliable[7].So in this work a new recognition system is presented, which is more reliable and effective if it is used in a complex mine environment. In this system, we improve the invariance by extracti

9、ng salient local image regions as landmarks to replace the whole image to deal with large changes in scale, 2D rotation and viewpoint. And the number of interest points is reduced effectively, which makes the processing

10、easier. Fuzzy recognition strategy is designed to recognize the landmarks in place of NN, which can strengthen the contribution of individual feature for scene recognition. Because of its partial information resuming abi

11、lity, hidden Markov model is adopted to organize those landmarks, which can capture the structure or relationship among them. So scene recognition can be transformed to the evaluation problem of HMM, which makes recognit

12、ion robust.2 Salient local image regions detectionResearches on biological vision system indicate that organism (like drosophila) often pays attention to certain special regions in the scene for their behavioral relevanc

13、e or local image cues while observing surroundings [8]. These regions can be taken as natural landmarks to effectively represent and distinguish different environments. Inspired by those, we use center-surround differenc

14、e method to detect salient regions in multi-scale image spaces. The opponencies of color and texture are computed to create the saliency map.Follow-up, sub-image centered at the salient position in S is taken as the land

15、mark region. The size of the landmark region can be decided adaptively according to the changes of gradient orientation of the local image [11].Mobile robot navigation requires that natural landmarks should be detected s

16、tably when environments change to some extent. To validate the repeatability on landmark detection of our approach, we have done some experiments on the cases of scale, 2D rotation and viewpoint changes etc. Fig.1 shows