光学学报, 2015, 35 (1): 0115003, 网络出版: 2014-12-15
基于多级直线表述和M-估计的三维目标位姿跟踪优化算法
Three Dimensional Rigid Objects Pose Tracking and Optimization Based on Multilevel Line Representation and M-Estimation
机器视觉 三维跟踪 直线表达 M-估计 粒子滤波 machine vision three dimensional tracking line representation M-estimation particle filter
摘要
为了实现复杂环境下已知模型目标姿态的快速跟踪和估计,提出了一种结合三维(3D)粒子滤波跟踪和M-估计优化的位姿跟踪估计算法。基于直线的多级向量表示构造了新颖的模型直线和图像直线相似性度量函数;基于粒子滤波跟踪的姿态设计了模型直线和图像直线快速对应方法;利用M-估计实现了目标姿态的优化估计;利用重要性采样方法将优化姿态有效地融合到了粒子滤波框架。另外根据预测的目标位姿定义了图像动态感兴趣区域(ROI),极大地减少了特征检测和搜索的时间。实验表明,所提方法能够实现复杂环境下自由移动目标的快速跟踪和位姿的高精度解算,相比已有方法,所提方法在跟踪精度,计算效率以及稳健性上均有优势。
Abstract
To track and estimate the pose and position of known rigid objects efficiently in complex environment, a method coupled three dimensional (3D) particle filter (PF) framework with M-estimation optimization in a closed loop is proposed. A novel similarity observation model is constructed based on multilevel line representation; line correspondences between 3D model edges and two dimensional (2D) image line segments are received easily based on the tracking state of PF. After that, line correspondences are provided for M-estimation to optimize the pose and position of objects. The optimized particles are fused into the particle filter framework according to the importance sampling theory. Moreover, to speed up the proposed method, line detection and search space is limited in a local region of interest (ROI) predicted by PF. Experiments show that the proposed method can effectively track and accurately estimate the pose of freely moving objects in unconstrained environment. Comparisons on synthetic and real images demonstrate that proposed method greatly outperforms the state-of-art method in accuracy and efficiency.
张跃强, 苏昂, 刘海波, 尚洋, 于起峰. 基于多级直线表述和M-估计的三维目标位姿跟踪优化算法[J]. 光学学报, 2015, 35(1): 0115003. Zhang Yueqiang, Su Ang, Liu Haibo, Shang Yang, Yu Qifeng. Three Dimensional Rigid Objects Pose Tracking and Optimization Based on Multilevel Line Representation and M-Estimation[J]. Acta Optica Sinica, 2015, 35(1): 0115003.