基于视觉定位识别的煤矿救灾机器人

中文翻译：
基于视觉定位识别的煤矿救灾机器人(中文4100字,英文3900字)
文摘:提出了一种基于模糊逻辑和隐马尔可夫模型(HMM) 新的场景识别系统,可以被应用在煤矿救灾等突发事件的定位机器人上。该系统使用单眼相机获取全方位形象的矿山环境用于机器人定位。采用中心外围差异的方法,凸显部分图像，提取自然地形。这些标志被HMM利用来构成场景,机器人用模糊逻辑策略来匹配的场景和标志。通过这种方法,定位问题 ,可以转化为评价问题, 这是现场识别问题的系统。这些方法的作用使系统有能力对付的大小、2 D旋转和观点的变化。实验结果证明,该系统在静态和动态两种情况能有较高的效率去识别和定位周围的环境。
关键词:机器人位置；场景识别； 凸显部分图像；匹配策略、模糊逻辑、隐马尔可夫模型

附录：
外文资料：
Scene recognition for mine rescue robot localization based on vision
Abstract: A new scene recognition system was presented 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 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 landmarks 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 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 recognition and localization in both static and dynamic mine environments.
Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model