面向室外环境的移动机器人场景识别与建模
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摘要
随着移动机器人种类及应用环境的不断增加,其应用领域已经从结构化室内环境逐步向准结构化或非结构化室外环境拓展。作为移动机器人定位、导航及环境探索等研究的基础,面向室外环境的场景识别与建模已成为移动机器人领域的研究热点之一。本文从三维场景中阶梯目标的检测与参数估计问题入手,进而对室外典型场景的分类与地形建模问题展开深入研究,从而为移动机器人的室外自主环境适应提供技术支持。
在移动机器人自主导航中,阶梯目标既可能被视为障碍物,也可能成为备选通路,还可能作为定位和导航的重要标识物。针对阶梯目标结构的多样性以及三维激光点云分布的不确定性,提出一种基于阶梯拓扑模型和模糊集理论的自适应阶梯目标检测与参数估计方法。根据阶梯剖面模型的拓扑关系,提出一种基于角度直方图算法的阶梯边缘点检测方法,提高了阶梯边缘位置估计的精度。采用同级线段提取与跨级线段接合策略,可实现候选阶梯边缘线集合的有效构建。通过模糊变换和自适应模糊推理实现各级候选阶梯边缘线之间的级联概率估计,并采用模拟退火算法搜索全局最优的候选阶梯边缘线组合,从而实现对阶梯三维模型的有效构建。
车体抖动及光照条件变化都会显著影响视觉图像的质量。在利用Gabor滤波进行图像增强的基础上,采用Canny算子实现阶梯边缘检测。提出局部融合相位编组法以增强低质量图像中边缘线提取的鲁棒性。经过边缘线段的接合与过滤,可获得机器人运动控制所需的偏移量参数。通过单目视觉图像与三维激光点云数据的特征级融合,提高机器人阶梯检测与参数估计的可靠性。
对于具有一定越障能力的移动机器人,地形分类与地形建模是判别环境可通过性的重要依据。针对室外地形复杂程度的差异,提出一种分级的地形分类与几何建模方法,为移动机器人的场景识别与运动规划提供参考。基于分层高程地图的地形表示方法,可实现对平坦路面上的可通行区域与障碍区域的快速判别。通过模糊推理生成一种表示自然场景内在模糊性的高级特征,并根据极大熵原理实现三维点云地形的分类,从而提高复杂地形条件下可通过性判别的准确性。在此基础上,利用地形片段的语义分割算法,实现室外场景中典型地形结构的建模。
分别在自主研发的野外无人车平台及沈阳自动化所研发的六轮腿机器人平台上进行实验验证,实验结果及数据分析验证了本文所提方法的有效性和实用性。
With the increasing in types and application of mobile robot, the application domain expands from structured indoor environment to quasi-structured or unstructured outdoor environment. As the basis of mobile robot's localization, navigation and exploration, scene recognition and modeling for outdoor environment become one of the hot topics in mobile robot. This dissertation starts form the problem of stair detection and parameter estimation in3D scene, and then conducts an in-depth study on classification and terrain modeling for typical outdoor scene, so as to provide technical support for the mobile robot's self-adaptation to outdoor environment.
In mobile robot's autonomous navigation, stairs can be seen as obstacles, alternative pathway and also important marks in localization and navigation. Targeting the structural diversity of stairs and distribution uncertainty of3D laser point cloud, an adaptive stair detection and parameter estimation method based on stair topology model and fuzzy set theory is proposed. According to the topological relations of staircase profile, a stair edge detection method based on Angle Histogram Algorithm is proposed to improve the estimation accuracy of the stair edge position. Adopting the in-level line extraction and cross-level line linking strategy, the candidate stair edge line set is constructed effectively. The cascade probability of candidate stair edges between levels is estimated by fuzzy transform and adaptive fuzzy reasoning. Global optimum candidate edge line combination is searched by Simulated Annealing Algorithm, so as to construct the3D stair model effectively.
The vehicle vibration and lighting conditions change will significantly affect the quality of vision image. By using Gabor Filter for image enhancement, the stair edge detection is then achieved by adopting Canny Operator. A local fusion phase grouping method is proposed to enhance the robustness of edge line extraction in low quality image. The offset parameter for robot motor control is obtained by linking and filtering of the edge lines. By the feature fusing of monocular vision image and3D laser point cloud data, the reliability of stair detection and parameter estimation is then enhanced.
For the mobile robot with certain ability of obstacle negotiation, terrain classification and terrain modeling are an important bases for the discrimination of environment passability. According to the diversity of outdoor terrain complexity, a hierarchical terrain classification and geometric modeling method is proposed for mobile robots'scene recognition and motion planning. Based on the terrain representation of layered elevation map, rapid discrimination of passable area and obstacle area on flat road surface can be realized. A senior feature representation of the inherent ambiguity of natural scene is produced by fuzzy inference, then terrain classification of3D point cloud is achieved according to the principle of maximum entropy, thereby the discrimination veracity of passability in outdoor scene is enhanced. Based on this, using semantic segmentation algorithm of terrain fragment, the modeling to typical terrain structure in outdoor scene is achieved.
The effectiveness and practicality of the methods proposed in this dissertation is verified with the experiments on the unmanned ground vehicle (UGV) developed by our research group and the leg-wheel robot developed by Shenyang Institute of Automation.
在移动机器人自主导航中,阶梯目标既可能被视为障碍物,也可能成为备选通路,还可能作为定位和导航的重要标识物。针对阶梯目标结构的多样性以及三维激光点云分布的不确定性,提出一种基于阶梯拓扑模型和模糊集理论的自适应阶梯目标检测与参数估计方法。根据阶梯剖面模型的拓扑关系,提出一种基于角度直方图算法的阶梯边缘点检测方法,提高了阶梯边缘位置估计的精度。采用同级线段提取与跨级线段接合策略,可实现候选阶梯边缘线集合的有效构建。通过模糊变换和自适应模糊推理实现各级候选阶梯边缘线之间的级联概率估计,并采用模拟退火算法搜索全局最优的候选阶梯边缘线组合,从而实现对阶梯三维模型的有效构建。
车体抖动及光照条件变化都会显著影响视觉图像的质量。在利用Gabor滤波进行图像增强的基础上,采用Canny算子实现阶梯边缘检测。提出局部融合相位编组法以增强低质量图像中边缘线提取的鲁棒性。经过边缘线段的接合与过滤,可获得机器人运动控制所需的偏移量参数。通过单目视觉图像与三维激光点云数据的特征级融合,提高机器人阶梯检测与参数估计的可靠性。
对于具有一定越障能力的移动机器人,地形分类与地形建模是判别环境可通过性的重要依据。针对室外地形复杂程度的差异,提出一种分级的地形分类与几何建模方法,为移动机器人的场景识别与运动规划提供参考。基于分层高程地图的地形表示方法,可实现对平坦路面上的可通行区域与障碍区域的快速判别。通过模糊推理生成一种表示自然场景内在模糊性的高级特征,并根据极大熵原理实现三维点云地形的分类,从而提高复杂地形条件下可通过性判别的准确性。在此基础上,利用地形片段的语义分割算法,实现室外场景中典型地形结构的建模。
分别在自主研发的野外无人车平台及沈阳自动化所研发的六轮腿机器人平台上进行实验验证,实验结果及数据分析验证了本文所提方法的有效性和实用性。
With the increasing in types and application of mobile robot, the application domain expands from structured indoor environment to quasi-structured or unstructured outdoor environment. As the basis of mobile robot's localization, navigation and exploration, scene recognition and modeling for outdoor environment become one of the hot topics in mobile robot. This dissertation starts form the problem of stair detection and parameter estimation in3D scene, and then conducts an in-depth study on classification and terrain modeling for typical outdoor scene, so as to provide technical support for the mobile robot's self-adaptation to outdoor environment.
In mobile robot's autonomous navigation, stairs can be seen as obstacles, alternative pathway and also important marks in localization and navigation. Targeting the structural diversity of stairs and distribution uncertainty of3D laser point cloud, an adaptive stair detection and parameter estimation method based on stair topology model and fuzzy set theory is proposed. According to the topological relations of staircase profile, a stair edge detection method based on Angle Histogram Algorithm is proposed to improve the estimation accuracy of the stair edge position. Adopting the in-level line extraction and cross-level line linking strategy, the candidate stair edge line set is constructed effectively. The cascade probability of candidate stair edges between levels is estimated by fuzzy transform and adaptive fuzzy reasoning. Global optimum candidate edge line combination is searched by Simulated Annealing Algorithm, so as to construct the3D stair model effectively.
The vehicle vibration and lighting conditions change will significantly affect the quality of vision image. By using Gabor Filter for image enhancement, the stair edge detection is then achieved by adopting Canny Operator. A local fusion phase grouping method is proposed to enhance the robustness of edge line extraction in low quality image. The offset parameter for robot motor control is obtained by linking and filtering of the edge lines. By the feature fusing of monocular vision image and3D laser point cloud data, the reliability of stair detection and parameter estimation is then enhanced.
For the mobile robot with certain ability of obstacle negotiation, terrain classification and terrain modeling are an important bases for the discrimination of environment passability. According to the diversity of outdoor terrain complexity, a hierarchical terrain classification and geometric modeling method is proposed for mobile robots'scene recognition and motion planning. Based on the terrain representation of layered elevation map, rapid discrimination of passable area and obstacle area on flat road surface can be realized. A senior feature representation of the inherent ambiguity of natural scene is produced by fuzzy inference, then terrain classification of3D point cloud is achieved according to the principle of maximum entropy, thereby the discrimination veracity of passability in outdoor scene is enhanced. Based on this, using semantic segmentation algorithm of terrain fragment, the modeling to typical terrain structure in outdoor scene is achieved.
The effectiveness and practicality of the methods proposed in this dissertation is verified with the experiments on the unmanned ground vehicle (UGV) developed by our research group and the leg-wheel robot developed by Shenyang Institute of Automation.
引文
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