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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >A Velocity-Based Rao-Blackwellized Particle Filter Approach to Monocular vSLAM
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A Velocity-Based Rao-Blackwellized Particle Filter Approach to Monocular vSLAM

机译：单眼vSLAM的基于速度的Rao-Blackwellized粒子滤波方法

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This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches; it is not applicable to certain formulations of the SLAM problem in which some of the states are not explicitly expressed in the measurement equation. This constraint impacts the versatility of the FastSLAM 2.0 in dealing with partially ob-servable systems, especially in dynamic environments where inclusion of higher order but unobservable states such as velocity and acceleration in the filtering process is highly desirable. In this paper, the formulation of an enhanced RBPF-based SLAM with proper sampling and importance weights calculation for resampling distributions is presented. As an example, the new formulation uses the higher order states of the pose of a monocular camera to carry out SLAM for a mobile robot. The results of the experiments on the robot verify the improved performance of the higher order RBPF under low parallax angles conditions.
机译：本文提出了一种改进的Rao-Blackwellized粒子滤波（RBPF）方法，用于仅解决单眼SLAM问题。虽然FastSLAM 2.0被认为是计算效率最高的SLAM方法之一;它不适用于某些状态未在测量方程中明确表示的SLAM问题的某些公式。此约束影响FastSLAM 2.0在处理部分可观察的系统时的多功能性，尤其是在动态环境中，其中非常需要在滤波过程中包含更高阶但不可观察的状态（例如速度和加速度）。在本文中，提出了一种基于RBPF的增强型SLAM的公式，该模型具有适当的采样和重要度权重计算，可用于重采样分布。例如，新的公式使用单眼相机姿态的高阶状态来执行针对移动机器人的SLAM。在机器人上进行的实验结果证明，在低视差角条件下，高阶RBPF的性能有所提高。

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