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 commonly adopted 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 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 Fig.4, the input image Fig.4(a) comes from different view of Fig.4(b). But the distance between Figs.4(a) and (b) computed by Jefferey divergence is larger than Fig.4(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 Figs.2(b) 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 is built by our laboratory. The vision system is composed of a CCD camera and a frame-grabber IVC-4200. The resolution of image is set to be 400×320 and the sample frequency is set to be 10 frames/s. The computer system is composed 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 of salient local image regions and distinguish similar scenes effectively. Table 1 shows the recognition result of static environments including 5 laneways and a silo. 10 scenes are selected from each environment and HMMs are created for each scene. Then 20 scenes are collected when the robot enters each environment subsequently to match the 60 HMMs above.

In the table, “truth” means that the scene to be localized matches with the right scene (the evaluation value of HMM is 30% greater than the second high evaluation). “Uncertainty” means that the evaluation value of HMM is greater than the second high evaluation under 10%. “Error match” means that the scene to be localized matches with the wrong scene. In the table, the ratio of error match is 0. But it is possible that the scene to be localized can?t match any scenes and new vertexes are created. Furthermore, the “ratio of truth” about silo is lower because salient cues are

fewer in this kind of environment.

In the period of automatic exploring, similar scenes can be combined. The process can be summarized as: when localization succeeds, the current landmark sequence is added to the accompanying observation sequence of the matched vertex un-repeatedly according to their orientation (including the angle of the image from which the salient local region and the heading of the robot come). The parameters of HMM are learned again.

Compared with the approaches using appearance features of the whole image (Method 2, M2), our system (M1) uses local salient regions to localize and map, which makes it have more tolerance of scale, viewpoint changes caused by robot?s movement and higher ratio of recognition and fewer amount of vertices on the topological map. So, our system has better performance in dynamic environment. These can be seen in Table 2. Laneways 1, 2, 4, 5 are in operation where some miners are working, which puzzle the robot.

6 Conclusions

1) Salient local image features are extracted to replace the whole image to participate in recognition, which improve the tolerance of changes in scale, 2D rotation and viewpoint of environment image.

2) Fuzzy logic is used to recognize the local image, and emphasize the individual feature?s contribution to recognition, which improves the reliability of landmarks.

3) HMM is used to capture the structure or relationship of those local images, which converts the scene recognition problem into the evaluation problem of HMM.

4) The results from the above experiments demonstrate that the mine rescue robot scene recognition system has higher ratio of recognition and localization.

Future work will be focused on using HMM to deal with the uncertainty of localization.

附录B 中文翻译

基于视觉的矿井救援机器人场景识别

CUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王 璐)

摘要：基于模糊逻辑和隐马尔可夫模型（HMM），论文提出了一个新的场景识别系

统，可应用于紧急情况下矿山救援机器人的定位。该系统使用单眼相机获取机器人所处位置的全方位的矿井环境图像。通过采用中心环绕差分法，从图像中提取突出的位置图像区域作为自然的位置标志。这些标志通过使用HMM有机组织起来代表机器人坐在场景，模糊逻辑算法用来匹配场景和位置标志。通过这种方式，定位问题，即系统的现场识别问题，可以转化为对HMM的评价问题。这些技术贡献使系统具有处理比率变化、二维旋转和视角变化的能力。实验结果还证明，该系统在静态和动态矿山环境中都具有较高的识别和定位的成功率。

关键字：机器人定位；场景识别；突出图像；匹配算法；模糊逻辑；隐马尔可夫模型

1 介绍

在机器人领域搜索和救援灾区是一个新兴而富有挑战性的课题。矿井救援机器人的开发是为了在紧急情况下进入矿井为被困人员查找可能的逃生路线，并确定该线路是否安全。定位识别是这个领域的基本问题。基于摄像头的定位可以主要分为几何法、拓扑法或混合法。凭借其可行性和有效性，场景识别成为拓扑定位的重要技术之一。

目前，大多数场景识别方法是基于全局图像特征，有两个不同的阶段：离线培训和在线匹配。

在训练阶段，机器人收集其所工作环境的图像，并处理这些图像提取出能表征该场景的全局特征。一些方法直接分析图像数据得到一些基本特征，比如PCA方法。但是，PCA方法是不能区分特征的类别。另一种方法使用外观特征包括颜色、纹理和边缘密度来表示图像。例如，周等人用多维直方图来描述全局外观特征。此方法简单，但对比率和光照变化敏感。事实上，各种全局图像特征，所受来自环境变化的影响。

LOWE提出了SIFT方法，该方法利用关注点尺度和方向所形成的描述的相似性获得特征。这些特征对于图像缩放、平移、旋转和局部光照不变是稳定的。但SIFT可能

产生1 000个或更多的兴趣点，这可能使处理器大大减慢。

在匹配阶段，近邻算法（NN）因其简单和可行而被广泛采用。但是它并不能捕捉到

个别特征对场景识别的贡献。在实验中，NN在表达两种部分之间的相似性时效果并不足够好。此外，所选的特征并不能彻底地按照国家模式识别标准表示场景，这使得识别结果不可靠。