基于视觉的矿井救援机器人场景识别英文文献翻译可编辑.docx

1、基于视觉的矿井救援机器人场景识别英文文献翻译可编辑基于视觉的矿井救援机器人场景识别-英文文献翻译 附录A 英文原文Scene recognition for mine rescue robot localization based on visionCUI Yi-an崔益安, CAI Zi-xing蔡自兴, WANG Lu王 璐Abstract:A new scene recognition system was presented based on fuzzy logic and hidden Markov modelHMM that can be applied in mine rescu

2、e robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landm

3、arks are organized by using HMM to represent the 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 evaluation problem of HMM. The contribut

4、ions 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 recognition and localization in both static and dynamic mine environments. Key words: robot location; scene recogn

5、ition; salient image; matching strategy; fuzzy logic; hidden Markov model1 Introduction Search and rescue in disaster area in the domain of robot is a burgeoning and challenging subject1. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those tra

6、pped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hybrid ones2. With its feasibility and effectiveness, scene recognition becomes on

7、e of the important technologies of topological localization. Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online. During the training stage, robot collects the images of the environment where it works and proc

8、esses the images to extract global features that represent the scene. Some approaches were used to analyze the data-set of image directly and some primary features were found, such as the PCA method 3. However, the PCA method is not effective in distinguishing the classes of features. Another type o

9、f approach uses appearance features including color, texture and edge density to represent the image. For example, ZHOU et al4 used multidimensional histograms to describe global appearance features. This method is simple but sensitive to scale and illumination changes. In fact, all kinds of global

10、image features are suffered from the change of environment. LOWE 5 presented a SIFT method that uses similarity invariant descriptors formed by characteristic scale and orientation at interest points to obtain the features. The features are invariant to image scaling, translation, rotation and parti

11、ally invariant to illumination changes. But SIFT may generate 1 000 or more interest points, which may slow down the processor dramatically. During the matching stage, nearest neighbor strategyNN is widely adopted for its facility and intelligibility6. But it cannot capture the contribution of indiv

12、idual feature for scene recognition. In experiments, 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 makes recognition not reliable7. So in thi

13、s 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 extracting salient local image regions as landmarks to replace the whole image to deal with large changes in scale, 2D rotation

14、 and viewpoint. And the number of interest points is reduced effectively, which makes the processing 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 inf

15、ormation resuming ability, 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 recognition robust. 2 Salient local image regions detection Researc

16、hes on biological vision system indicate that organism like drosophila often pays attention to certain special regions in the scene for their behavioral relevance or local image cues while observing surroundings 8. These regions can be taken as natural landmarks to effectively represent and distingu

17、ish different environments. Inspired by those, we use center-surround difference 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

18、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 stably when environments change to some extent. To validate the repeatability on l

19、andmark detection of our approach, we have done some experiments on the cases of scale, 2D rotation and viewpoint changes etc. Fig.1 shows that the door is detected for its saliency when viewpoint changes. More detailed analysis and results about scale and rotation can be found in our previous works

20、12.3 Scene recognition and localization Different from other scene recognition systems, our system doesnt need training offline. In other words, our scenes are not classified in advance. When robot wanders, scenes captured at intervals of fixed time are used to build the vertex of a topological map,

21、 which represents the place where robot locates. Although the maps geometric layout is ignored by the localization system, it is useful for visualization and debugging13 and beneficial to path planning. So localization means searching the best match of current scene on the map. In this paper hidden

22、Markov model is used to organize the extracted landmarks from current scene and create the vertex of topological map for its partial information resuming ability. Resembled by panoramic vision system, robot looks around to get omni-images. From Fig.1 Experiment on viewpoint changes each image, salie

23、nt local regions are detected and formed to be a sequence, named as landmark sequence whose order is the same as the image sequence. Then a hidden Markov model is created based on the landmark sequence involving k salient local image regions, which is taken as the description of the place where the

24、robot locates. In our system EVI-D70 camera has a view field of 170. Considering the overlap effect, we sample environment every 45 to get 8 images Let the 8 images as hidden state Si 1i8, the created HMM can be illustrated by Fig.2. The parameters of HMM, aij and bjk, are achieved by learning, usin

25、g Baulm-Welch algorithm14. The threshold of convergence is set as 0.001. As for the edge of topological map, we assign it with distance information between two vertices. The distances can be computed according to odometry readings. Fig.2 HMM of environment To locate itself on the topological map, ro

26、bot must run its eye on environment and extract a landmark sequence L1Lk , then search the map for the best matched vertex scene. Different from traditional probabilistic localization15, in our system localization problem can be converted to the evaluation problem of HMM. The vertex with the greates

27、t 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

28、 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 commonly ado

29、pted in the community 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 it5. ASM and ENT: Angular second moment and entropy, which are two texture descriptors. H: Hue, which is used to descr

30、ibe 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 cant adequately exhibit the indi

31、vidual descriptor or features contribution to similarity measurement. As indicated in Fig.4, the input image Fig.4a comes from different view of Fig.4b. But the distance between Figs.4a and b computed by Jefferey divergence is larger than Fig.4c. To solve the problem, we design a new match algorithm

32、 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 Figs.2b and c are demonstrated by Fig.3. As indicated, this method can measure the similarity effectively between two patterns. Fig.3 Similarity computed using fuzzy strategy 5 Experiments and analysis The localization system has been implemented on a mobile robot, which i