外文文獻及翻譯基于視覺的礦井救援機器人場景識別（英文）

1、 Scene recognition for mine rescue robot localization based on vision CUI Yi-an(崔益安)1, 2, CAI Zi-xing(蔡自興)1, WANG Lu(王 璐)1 1. School of Information Science and Engineering, Central South University, Changsha 410083, Chin

2、a; 2. School of Info-Physics Engineering, Central South University, Changsha 410083, China Received 18 April 2007; accepted 13 September 2007 Abstract: A new scene recognition system was presented based on fuzzy logic a

3、nd 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 environment where the robot locates.

4、 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 scene where the robot is,

5、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 con

6、tributions 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 localizati

7、on in both static and dynamic mine environments. Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model 1 Introduction Search and rescue in disaster area in the d

8、omain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe

9、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 ones[2]. With its feasibility and ef

10、fectiveness, scene recognition becomes one of the important technologies of topological localization. Currently most scene recognition methods are based on global image features and have two distinct stages: trainin

11、g offline and matching online. During the training stage, robot collects the images of the environment where it works and processes the images to extract global features that represent the scene. Some approaches were

12、 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 of app

13、roach uses appearance features including color, texture and edge density to represent the image. For example, ZHOU et al[4] used multi- dimensional histograms to describe global appearance features. This method is sim

14、ple but sensitive to scale and illumination changes. In fact, all kinds of global image features are suffered from the change of environment. LOWE[5] presented a SIFT method that uses similarity invariant descriptors

15、 formed by characteristic scale and orientation at interest points to obtain the features. The features are invariant to image scaling, translation, rotation and partially invariant to illumination changes. But

16、SIFT may generate 1 000 or more interest points, which may slow down the processor dramatically. During the matching stage, nearest neighbor strategy(NN) is widely adopted for its facility and intelligibility[6]. B

17、ut it cannot capture the contribution of individual 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

18、represent the scene thoroughly according to the state-of-art pattern recognition, which makes recognition not reliable[7]. So in this work a new recognition system is presented, which is more reliable and effective i

19、f it is used Foundation item: Project(60234030) supported by the National Natural Science Foundation of China; Project(A1420060159) supported by the Basic Research Program of the 11th Five-Year-Plan of China Correspond

20、ing author: CUI Yi-an; Tel: +86-731-8877075; E-mail: csu-iag@mail.csu.edu.cn CUI Yi-an, et al/Trans. Nonferrous Met. Soc. China 18(2008) 434 Fig.1 Saliency detection on real mine images: (a) Original image, (b) Obtained

21、 landmark regions Fig.2 Experiment on viewpoint changes 3 Scene recognition and localization Different from other scene recognition systems, our system doesn’t need training offline. In other words, our scenes are not

22、classified in advance. When robot wanders, scenes captured at intervals of fixed time are used to build the vertex of a topological map, which represents the place where robot locates. Although the map’s geometric la

23、yout is ignored by the localization system, it is useful for visualization and debugging[13] and beneficial to path planning. So localization means searching the best match of current scene on the map. In this paper

24、hidden 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 loo

25、ks around to get omni-images. From each image, salient 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

26、created based on the landmark sequence involving k salient local image regions, which is taken as the description of the place where the robot locates. In our system EVI-D70 camera has a view field of ±170?. Con

27、sidering the overlap effect, we sample environment every 45? to get 8 images. Let the 8 images as hidden state Si (1≤i≤8), the created HMM can be illustrated by Fig.3. The parameters of HMM, aij and bjk, are achieve