自主移动机器人全向视觉系统研究
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摘要
本论文在设计实现了机器人全向视觉系统的基础上,重点研究了如何提高全向视觉系统的鲁棒性,并将其应用于自主移动机器人,实现鲁棒的目标识别和自定位。
具有环境感知能力是移动机器人实现自主的基础,而视觉传感器是一种能为自主移动机器人提供最为丰富的环境信息的传感器。在各种视觉传感器中,全向视觉装置由于具有360°的水平视场角,能够在一幅图像中获取机器人周围环境的全景信息,经过图像处理、分析和理解可实现机器人的目标识别、建图、自定位等,因此已经在各种移动机器人中得到了越来越广泛的应用。尽管全向视觉在自主移动机器人中的应用已经取得了很多研究成果,但是由于该问题本身的复杂性和高难度,因此仍然面临很多的挑战。这些挑战中既有计算机/机器人视觉研究中面临的共性问题,又有由全向视觉的成像特性所带来的问题。例如在动态光线条件下,如何使机器人视觉的图像获取及图像处理和理解具有恒常性;机器人目前在复杂的非结构化环境中工作时所使用的局部视觉特征提取算法往往计算量过大,无法应用于具有较高实时性要求的实际工程问题,同时针对全向视觉的成像特性,局部视觉特征提取算法还要做相应的调整;如何提高利用全向视觉对各种普通目标的识别能力,以降低对机器人工作环境的约束;如何使机器人在室内结构化环境中的自定位对环境高度动态因素具有鲁棒性;针对机器人在非结构化环境中的自定位问题,如何更多的借鉴模式识别领域中的大量研究成果,使机器人认知环境和实现定位的工作方式与人类更加相符;等等。
针对上述挑战,本文主要开展并完成了以下几个方面的研究工作:
(1)全向视觉系统的设计与标定。较为完整地总结了折反射式全向视觉系统的设计及标定问题,并以RoboCup中型组比赛用机器人为实验对象,设计实现了一种新的使用组合反射镜面的NuBot全向视觉系统,给出了一个进行全向视觉系统设计的范例。根据该全向视觉系统非单视点成像的特点,采用一种免模型的标定思想,完成了该系统较为精确的距离映射标定。
(2)基于图像熵的摄像机参数自动调节算法。光线条件的变化会极大的影响视觉系统采集到的图像,进而给机器人的目标识别、自定位等造成很大的困难。因此在定义了图像熵后,通过实验验证了图像熵能够有效地表征摄像机参数是否设置恰当,进而提出了一种基于图像熵的摄像机参数自动调节算法,使视觉系统输出的图像具有一定的恒常性,以提高机器人视觉系统对光线条件变化的鲁棒性。使用NuBot全向视觉系统和普通透视成像摄像机在室内外环境中开展实验,结果验证了该算法的有效性。
(3)两种实时的应用于全向视觉的局部视觉特征提取算法。针对目前现有算法难以实时提取局部视觉特征的问题,提出了两种新的用于全向视觉的实时局部视觉特征提取算法,称为FAST+LBP和FAST+CSLBP,以使得局部视觉特征能够应用于实时性要求较高的实际工程问题。使用COLD图像数据库进行全景图像特征匹配实验,确定了算法的最优参数设置,并与经典的SIFT算法作性能和计算时间上的比较,验证了所提出的局部视觉特征算法的有效性和实时性。
(4)基于全向视觉的足球机器人任意足球识别方法。通过分析足球在NuBot全向视觉系统中的成像特性,得出结论为其在全景图像中成像为椭圆,然后根据该成像特性设计了相应的图像处理算法实现对任意FIFA足球的检测,还结合球速估计算法实现了对任意FIFA足球的实时跟踪。该识别过程不依赖于足球的彩色信息,因此该研究既可以提高机器人全向视觉系统的鲁棒性,又可以降低RoboCup中型组比赛环境的颜色编码化程度以促进RoboCup最终目标的实现。实验结果验证了算法的有效性。
(5)高度动态的室内结构化环境中基于全向视觉的机器人自定位。以RoboCup中型组比赛为应用背景和测试环境,设计了一种新的基于全向视觉的机器人自定位算法,结合目前最常用的两种定位算法即粒子滤波和匹配优化定位算法的优点,并弥补了各自的不足,以在实时获得高精度的自定位的同时实现有效的全局定位,同时结合使用基于图像熵的摄像机参数自动调节算法,使定位算法对环境的各种动态因素如视觉系统被大量遮挡、机器人高速动态对抗、变化的光线条件等具有很强的鲁棒性。在标准的RoboCup中型组比赛环境中开展一系列实验,结果验证了算法的有效性。
(6)非结构化环境中基于全向视觉的机器人拓扑定位。非结构化环境中机器人导航定位等问题所使用的视觉特征往往是人类无法直接理解的,直接基于特征匹配的机器人拓扑定位则需要大量的存储空间,且与人类认识环境的工作方式也是不相符的。因此将模式识别领域中得到了很多成功应用的特征袋方法引入到机器人定位问题中,结合使用所提出的两种实时局部视觉特征和基于统计学习思想的支持向量机分类器学习算法,得到了一种基于局部视觉特征袋和支持向量机的机器人地点识别算法,并用于实现机器人的拓扑定位。使用COLD图像数据库开展实验研究,确定了算法最优的参数和训练条件,并验证了算法的有效性。
On the basis of designing and realizing the omnidirectional vision system for autonomous mobile robots, the thesis focuses on how to improve the robustness of the omnidirectional vision system, and how to achieve robust object recognition and self-localization based on omnidirectional vision for autonomous mobile robots.
The autonomy of mobile robots depends on the ability to percept the environment, and visual sensors can provide the richest environment information for autonomous mobile robots. Among all of the visual sensors, the omnidirectional vision system can provide a 360o view of the robot’s surrounding environment in a single panoramic im-age, and the robot can use it to realize object recognition, mapping, self-localization, etc. by image processing, analyzing and understanding. So it has become more and more popular as a visual sensor for autonomous mobile robots. Although a lot of progresses have been achieved in the applications of omnidirectional vision for autonomous mobile robots, many challenges still exist because of the complexity and the difficulty of this problem. These challenges include not only the common problems in computer/robot vision research, but also the problems brought forth by the special imaging character of omnidirectional vision. For example, how to achieve the constancy in the image acqui-sition, processing and understanding of the robot vision is still challenging under dy-namic lighting conditions; the computation costs of the existing local visual feature al-gorithms used by robots when working in the complex and unstructured environment are usually high, which limits the actual application of local visual features in the engi-neering situations with high real-time requirements; the local visual feature algorithms should be adjusted according to the special imaging character of omnidirectional vision; the ability to recognize the ordinary objects based on omnidirectional vision should be improved to reduce the constraints of the robot’s working environment; the robot’s self-localization should be robust with respect to the highly dynamic factors in the in-door and structured environment; more research results in the pattern recognition com-munity should be introduced and applied to the robot’s self-localization in the unstruc-tured environment, so the robot can cognize the environment and realize self-localization in a more consistent way with human; etc..
The following research is performed and finished in the thesis to deal with the challenges mentioned above:
(1) The design and calibration of the omnidirectional vision system. The design and calibration of the catadioptric omnidirectional vision system are summarized com-prehensively in the thesis, and then a novel omnidirectional vision system named as NuBot is designed and realized by using RoboCup Middle Size League (MSL) soccer robots as the test-bed, which also provides an example on how to design the omnidirec- tional vision system. Because the NuBot omnidirectional vision system is not a single viewpoint one, a model-free calibration idea is adopted to perform the calibration of the distance map for the system.
(2) Camera parameters auto-adjusting technique based on image entropy. The varying lighting conditions will affect the images acquired by vision systems, which will cause much difficulty to the robot’s object recognition and self-localization. In the thesis, the definition of image entropy is presented, and then it is verified that image en-tropy can indicate whether camera parameters are set properly by experiments. A cam-era parameters auto-adjusting algorithm based on image entropy is proposed to achieve some kind of color constancy in the output of vision systems, so the robustness to the varying lighting conditions can be improved for the robot’s vision system. The algo-rithm is tested by using the NuBot omnidirectional vision system and the perspective camera in indoor and outdoor environments, and the results show that the algorithm is effective.
(3) Two novel real-time local visual features for omnidirectional vision. To deal with that local visual features can not be extracted in real-time by the existing algo-rithms, two novel real-time local visual features, namely FAST+LBP and FAST+CSLBP, are proposed in the thesis for omnidirectional vision to make local vis-ual features applicable in the actual engineering problems with high real-time require-ments. The matching experiments of the panoramic images from the COLD database are performed to determine their optimal parameters, and to compare with the famous SIFT in performance and computation cost. The experimental results show that the proposed local visual features perform better, and can be extracted in real-time.
(4) Arbitrary FIFA ball recognition based on omnidirectional vision for soccer ro-bots. The conclusion is derived that the ball is imaged to be the ellipse in the panoramic image acquired by the NuBot omnidirectional vision system by analyzing the imaging character, and then an image processing algorithm is designed to detect the arbitrary FIFA ball according to this imaging character. A ball velocity estimating algorithm is also integrated to track the ball in real-time. The recognition process is independent on the color information, so the robustness of the robot’s omnidirectional vision can be improved, and the current color-coded environment of RoboCup MSL can be further changed to promote the realization of the final goal of RoboCup. The experimental re-sults validate the effectiveness of the algorithm.
(5) Robot’s robust self-localization based on omnidirectional vision in the highly dynamic indoor and structured environment. Taking the RoboCup MSL competition as the application background and the test-bed, a novel self-localization method based on omnidirectional vision is proposed in the thesis. The method combines the particle filter localization and the matching optimization localization, which are two most popular ones currently, by utilizing their virtue and making up their respective deficiency. So the highly accurate self-localization can be achieved in real-time while global localiza-tion is realized effectively. By integrating the camera parameters auto-adjusting algo-rithm based on image entropy, the robot’s self-localization is robust to the highly dy-namic environment with occlusions, high speed competing between robots and varying lighting conditions. The experimental results in the standard RoboCup MSL field show that the algorithm is effective.
(6) Robot’s topological self-localization based on omnidirectional vision in the un-structured environment. In the unstructured environment, the visual features applied in the robot’s navigation and localization can not be understood by human, and the robot’s topological self-localization based on feature matching directly needs huge memory space, which is inconsistent with the way human cognize the environment. The bag of features method, which is popular and has been applied successfully in pattern recogni-tion problems, is introduced to the robot’s self-localization in the thesis. By combing the two real-time local visual features proposed in this thesis and Support Vector Machines (SVM) classifier learning algorithm based on statistical learning, a place recognition algorithm based on bag of local visual features and SVM is presented for mobile robots, meanwhile the robot’s topological self-localization is also realized by place recognition. The COLD database is used to perform the experiments to determine the best algorithm parameters and training conditions, and the results verify the effectiveness of the algo-rithm.
具有环境感知能力是移动机器人实现自主的基础,而视觉传感器是一种能为自主移动机器人提供最为丰富的环境信息的传感器。在各种视觉传感器中,全向视觉装置由于具有360°的水平视场角,能够在一幅图像中获取机器人周围环境的全景信息,经过图像处理、分析和理解可实现机器人的目标识别、建图、自定位等,因此已经在各种移动机器人中得到了越来越广泛的应用。尽管全向视觉在自主移动机器人中的应用已经取得了很多研究成果,但是由于该问题本身的复杂性和高难度,因此仍然面临很多的挑战。这些挑战中既有计算机/机器人视觉研究中面临的共性问题,又有由全向视觉的成像特性所带来的问题。例如在动态光线条件下,如何使机器人视觉的图像获取及图像处理和理解具有恒常性;机器人目前在复杂的非结构化环境中工作时所使用的局部视觉特征提取算法往往计算量过大,无法应用于具有较高实时性要求的实际工程问题,同时针对全向视觉的成像特性,局部视觉特征提取算法还要做相应的调整;如何提高利用全向视觉对各种普通目标的识别能力,以降低对机器人工作环境的约束;如何使机器人在室内结构化环境中的自定位对环境高度动态因素具有鲁棒性;针对机器人在非结构化环境中的自定位问题,如何更多的借鉴模式识别领域中的大量研究成果,使机器人认知环境和实现定位的工作方式与人类更加相符;等等。
针对上述挑战,本文主要开展并完成了以下几个方面的研究工作:
(1)全向视觉系统的设计与标定。较为完整地总结了折反射式全向视觉系统的设计及标定问题,并以RoboCup中型组比赛用机器人为实验对象,设计实现了一种新的使用组合反射镜面的NuBot全向视觉系统,给出了一个进行全向视觉系统设计的范例。根据该全向视觉系统非单视点成像的特点,采用一种免模型的标定思想,完成了该系统较为精确的距离映射标定。
(2)基于图像熵的摄像机参数自动调节算法。光线条件的变化会极大的影响视觉系统采集到的图像,进而给机器人的目标识别、自定位等造成很大的困难。因此在定义了图像熵后,通过实验验证了图像熵能够有效地表征摄像机参数是否设置恰当,进而提出了一种基于图像熵的摄像机参数自动调节算法,使视觉系统输出的图像具有一定的恒常性,以提高机器人视觉系统对光线条件变化的鲁棒性。使用NuBot全向视觉系统和普通透视成像摄像机在室内外环境中开展实验,结果验证了该算法的有效性。
(3)两种实时的应用于全向视觉的局部视觉特征提取算法。针对目前现有算法难以实时提取局部视觉特征的问题,提出了两种新的用于全向视觉的实时局部视觉特征提取算法,称为FAST+LBP和FAST+CSLBP,以使得局部视觉特征能够应用于实时性要求较高的实际工程问题。使用COLD图像数据库进行全景图像特征匹配实验,确定了算法的最优参数设置,并与经典的SIFT算法作性能和计算时间上的比较,验证了所提出的局部视觉特征算法的有效性和实时性。
(4)基于全向视觉的足球机器人任意足球识别方法。通过分析足球在NuBot全向视觉系统中的成像特性,得出结论为其在全景图像中成像为椭圆,然后根据该成像特性设计了相应的图像处理算法实现对任意FIFA足球的检测,还结合球速估计算法实现了对任意FIFA足球的实时跟踪。该识别过程不依赖于足球的彩色信息,因此该研究既可以提高机器人全向视觉系统的鲁棒性,又可以降低RoboCup中型组比赛环境的颜色编码化程度以促进RoboCup最终目标的实现。实验结果验证了算法的有效性。
(5)高度动态的室内结构化环境中基于全向视觉的机器人自定位。以RoboCup中型组比赛为应用背景和测试环境,设计了一种新的基于全向视觉的机器人自定位算法,结合目前最常用的两种定位算法即粒子滤波和匹配优化定位算法的优点,并弥补了各自的不足,以在实时获得高精度的自定位的同时实现有效的全局定位,同时结合使用基于图像熵的摄像机参数自动调节算法,使定位算法对环境的各种动态因素如视觉系统被大量遮挡、机器人高速动态对抗、变化的光线条件等具有很强的鲁棒性。在标准的RoboCup中型组比赛环境中开展一系列实验,结果验证了算法的有效性。
(6)非结构化环境中基于全向视觉的机器人拓扑定位。非结构化环境中机器人导航定位等问题所使用的视觉特征往往是人类无法直接理解的,直接基于特征匹配的机器人拓扑定位则需要大量的存储空间,且与人类认识环境的工作方式也是不相符的。因此将模式识别领域中得到了很多成功应用的特征袋方法引入到机器人定位问题中,结合使用所提出的两种实时局部视觉特征和基于统计学习思想的支持向量机分类器学习算法,得到了一种基于局部视觉特征袋和支持向量机的机器人地点识别算法,并用于实现机器人的拓扑定位。使用COLD图像数据库开展实验研究,确定了算法最优的参数和训练条件,并验证了算法的有效性。
On the basis of designing and realizing the omnidirectional vision system for autonomous mobile robots, the thesis focuses on how to improve the robustness of the omnidirectional vision system, and how to achieve robust object recognition and self-localization based on omnidirectional vision for autonomous mobile robots.
The autonomy of mobile robots depends on the ability to percept the environment, and visual sensors can provide the richest environment information for autonomous mobile robots. Among all of the visual sensors, the omnidirectional vision system can provide a 360o view of the robot’s surrounding environment in a single panoramic im-age, and the robot can use it to realize object recognition, mapping, self-localization, etc. by image processing, analyzing and understanding. So it has become more and more popular as a visual sensor for autonomous mobile robots. Although a lot of progresses have been achieved in the applications of omnidirectional vision for autonomous mobile robots, many challenges still exist because of the complexity and the difficulty of this problem. These challenges include not only the common problems in computer/robot vision research, but also the problems brought forth by the special imaging character of omnidirectional vision. For example, how to achieve the constancy in the image acqui-sition, processing and understanding of the robot vision is still challenging under dy-namic lighting conditions; the computation costs of the existing local visual feature al-gorithms used by robots when working in the complex and unstructured environment are usually high, which limits the actual application of local visual features in the engi-neering situations with high real-time requirements; the local visual feature algorithms should be adjusted according to the special imaging character of omnidirectional vision; the ability to recognize the ordinary objects based on omnidirectional vision should be improved to reduce the constraints of the robot’s working environment; the robot’s self-localization should be robust with respect to the highly dynamic factors in the in-door and structured environment; more research results in the pattern recognition com-munity should be introduced and applied to the robot’s self-localization in the unstruc-tured environment, so the robot can cognize the environment and realize self-localization in a more consistent way with human; etc..
The following research is performed and finished in the thesis to deal with the challenges mentioned above:
(1) The design and calibration of the omnidirectional vision system. The design and calibration of the catadioptric omnidirectional vision system are summarized com-prehensively in the thesis, and then a novel omnidirectional vision system named as NuBot is designed and realized by using RoboCup Middle Size League (MSL) soccer robots as the test-bed, which also provides an example on how to design the omnidirec- tional vision system. Because the NuBot omnidirectional vision system is not a single viewpoint one, a model-free calibration idea is adopted to perform the calibration of the distance map for the system.
(2) Camera parameters auto-adjusting technique based on image entropy. The varying lighting conditions will affect the images acquired by vision systems, which will cause much difficulty to the robot’s object recognition and self-localization. In the thesis, the definition of image entropy is presented, and then it is verified that image en-tropy can indicate whether camera parameters are set properly by experiments. A cam-era parameters auto-adjusting algorithm based on image entropy is proposed to achieve some kind of color constancy in the output of vision systems, so the robustness to the varying lighting conditions can be improved for the robot’s vision system. The algo-rithm is tested by using the NuBot omnidirectional vision system and the perspective camera in indoor and outdoor environments, and the results show that the algorithm is effective.
(3) Two novel real-time local visual features for omnidirectional vision. To deal with that local visual features can not be extracted in real-time by the existing algo-rithms, two novel real-time local visual features, namely FAST+LBP and FAST+CSLBP, are proposed in the thesis for omnidirectional vision to make local vis-ual features applicable in the actual engineering problems with high real-time require-ments. The matching experiments of the panoramic images from the COLD database are performed to determine their optimal parameters, and to compare with the famous SIFT in performance and computation cost. The experimental results show that the proposed local visual features perform better, and can be extracted in real-time.
(4) Arbitrary FIFA ball recognition based on omnidirectional vision for soccer ro-bots. The conclusion is derived that the ball is imaged to be the ellipse in the panoramic image acquired by the NuBot omnidirectional vision system by analyzing the imaging character, and then an image processing algorithm is designed to detect the arbitrary FIFA ball according to this imaging character. A ball velocity estimating algorithm is also integrated to track the ball in real-time. The recognition process is independent on the color information, so the robustness of the robot’s omnidirectional vision can be improved, and the current color-coded environment of RoboCup MSL can be further changed to promote the realization of the final goal of RoboCup. The experimental re-sults validate the effectiveness of the algorithm.
(5) Robot’s robust self-localization based on omnidirectional vision in the highly dynamic indoor and structured environment. Taking the RoboCup MSL competition as the application background and the test-bed, a novel self-localization method based on omnidirectional vision is proposed in the thesis. The method combines the particle filter localization and the matching optimization localization, which are two most popular ones currently, by utilizing their virtue and making up their respective deficiency. So the highly accurate self-localization can be achieved in real-time while global localiza-tion is realized effectively. By integrating the camera parameters auto-adjusting algo-rithm based on image entropy, the robot’s self-localization is robust to the highly dy-namic environment with occlusions, high speed competing between robots and varying lighting conditions. The experimental results in the standard RoboCup MSL field show that the algorithm is effective.
(6) Robot’s topological self-localization based on omnidirectional vision in the un-structured environment. In the unstructured environment, the visual features applied in the robot’s navigation and localization can not be understood by human, and the robot’s topological self-localization based on feature matching directly needs huge memory space, which is inconsistent with the way human cognize the environment. The bag of features method, which is popular and has been applied successfully in pattern recogni-tion problems, is introduced to the robot’s self-localization in the thesis. By combing the two real-time local visual features proposed in this thesis and Support Vector Machines (SVM) classifier learning algorithm based on statistical learning, a place recognition algorithm based on bag of local visual features and SVM is presented for mobile robots, meanwhile the robot’s topological self-localization is also realized by place recognition. The COLD database is used to perform the experiments to determine the best algorithm parameters and training conditions, and the results verify the effectiveness of the algo-rithm.
引文
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