马鑫, 梁新武. RGB-D相机位姿估计不确定性与观测参数化分析[J]. 机器人, 2021, 43(1): 54-65,73. DOI: 10.13973/j.cnki.robot.200026
引用本文: 马鑫, 梁新武. RGB-D相机位姿估计不确定性与观测参数化分析[J]. 机器人, 2021, 43(1): 54-65,73. DOI: 10.13973/j.cnki.robot.200026
MA Xin, LIANG Xinwu. Analysis on Pose Estimation Uncertainty and Observation Parametrization for RGB-D Cameras[J]. ROBOT, 2021, 43(1): 54-65,73. DOI: 10.13973/j.cnki.robot.200026
Citation: MA Xin, LIANG Xinwu. Analysis on Pose Estimation Uncertainty and Observation Parametrization for RGB-D Cameras[J]. ROBOT, 2021, 43(1): 54-65,73. DOI: 10.13973/j.cnki.robot.200026

RGB-D相机位姿估计不确定性与观测参数化分析

Analysis on Pose Estimation Uncertainty and Observation Parametrization for RGB-D Cameras

  • 摘要: 针对基于RGB-D相机的位姿估计问题,根据最大似然估计和协方差传播规律,分析了ICP (迭代最近点)与PnP (n点透视)两种算法的位姿估计不确定性;并从数据关联的角度探究了2种算法在实际应用中的差异.此外,对比了不同的特征观测参数化形式对视觉里程计定位精度的影响,并提出了一种参数化方法.基于ICP与PnP算法的区别与联系,提出RGB-D SLAM (同步定位与地图创建)系统中2种PnP算法的切换策略,并在优化问题中根据观测的不确定性对各误差项赋予不同权重.在两大公开数据集上的实验表明,与主流RGB-D SLAM算法相比本文所提算法在弱纹理、相机快速运动和动态物体等多种场景下具有更高的定位精度和鲁棒性.同时,本文所提算法的时间效率较ORB-SLAM2算法提高了约10%.

     

    Abstract: For the pose estimation based on RGB-D cameras, the pose estimation uncertainties of the ICP (iterative closest point) and PnP (perspective-n-point) algorithms are analyzed according to the maximum likelihood estimation and covariance propagation law, and the differences between the two algorithms in practical applications are explored from the perspective of data association. In addition, the influence of different parametrization forms of feature observation on the localization accuracy of visual odometer is compared, and a parametrization method is proposed. Based on the difference and connection between ICP and PnP algorithms, a switching strategy for the two PnP algorithms in the RGB-D SLAM (simultaneous localization and mapping) system is proposed, and different weights are assigned to each error term in the optimization problem according to the uncertainty of the observation. Experiments on two public datasets show that compared with the mainstream RGB-D SLAM algorithms, the proposed algorithm has higher localization accuracy and robustness in a variety of scenes with low texture, fast camera movement or dynamic objects. Meanwhile, the time efficiency of the proposed algorithm is about 10% higher than ORB-SLAM2 algorithm.

     

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