A probabilistic framework for object search with 6-DOF pose estimation
A probabilistic framework for object search with 6-DOF pose estimation
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DOI：
10.1177/0278364911410090
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
Jeremy Ma;Timothy H. Chung;J. Burdick
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文献类型：
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作者：
Jeremy Ma;Timothy H. Chung;J. Burdick
本文提出了一种系统的方法，在室内环境中的自主3D对象搜索的问题，使用两轮非完整机器人配备了一个驱动的立体摄像头和处理在一台笔记本电脑上完成。基于概率网格的地图编码每个单元中对象存在的可能性，并在每次感测动作后更新。更新模式结合了机器人的传感模式后建模的特征参数，并允许通过贝叶斯递归方法的顺序更新。两种类型的感测模态用于更新地图：基于颜色直方图方法的粗略搜索方法（全局搜索）和基于尺度不变特征变换（SIFT）特征匹配的更精细的搜索方法（局部搜索）。如果局部搜索正确地定位了所需的对象，则使用应用于每个SIFT特征（即3D SIFT特征）的立体声来估计其6-DOF姿态，然后将其作为测量值馈送到扩展卡尔曼滤波器（EKF）中以进行持续跟踪。如果局部搜索未能在特定单元中定位期望对象，则在概率图中更新该单元，并且使用经由来自立体的障碍物检测填充的单独的基于网格的成本图来识别和规划下一个峰值概率单元，其中使用A* 规划器完成规划。从使用这种方法在移动的机器人上获得的实验结果来说明和验证的方法，确认搜索策略可以进行适度的计算在一台笔记本电脑上。
This article presents a systematic approach to the problem of autonomous 3D object search in indoor environments, using a two-wheeled non-holonomic robot equipped with an actuated stereo-camera head and processing done on a single laptop. A probabilistic grid-based map encodes the likelihood of object existence in each cell and is updated after each sensing action. The updating schema incorporates characteristic parameters modeled after the robot’s sensing modalities and allows for sequential updating via Bayesian recursion methods. Two types of sensing modalities are used to update the map: a coarse search method (global search) based on a color histogram approach, and a more refined search method (local search) based on Scale-Invariant Feature Transform (SIFT) feature matching. If the local search correctly locates the desired object, its 6-DOF pose is estimated using stereo applied to each SIFT feature (i.e. 3D SIFT feature), which is then fed as measurements into an Extended Kalman Filter (EKF) for sustained tracking. If the local search fails to locate the desired object in a particular cell, the cell is updated in the probability map and the next peak probability cell is identified and planned to using a separate grid-based costmap populated via obstacle detection from stereo, with planning done using an A* planner. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation on a single laptop.