A study of the rao-blackwellised particle filter for efficient and accurate vision-based SLAM
A study of the rao-blackwellised particle filter for efficient and accurate vision-based SLAM
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DOI：
10.1007/s11263-006-0021-0
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发表时间：
2007-09-01
影响因子：
19.5
通讯作者：
Little, James J.
中科院分区：
文献类型：
--
作者：
Sim, Robert;Elinas, Pantelis;Little, James J.
随着实时实现用于解决距离传感领域中的同时定位和映射(SLAM)问题的滤波器的最新进展，人们的注意力已经转移到使用基于视觉的传感来实现SLAM解决方案。提出并分析了基于视觉SLAM的Rao-Blackwell ized粒子滤波(RBPF)的不同模型。我们的主要工作是引入了一种新的利用运动结构(SFM)方法的机器人运动模型，以及一种结合了局部和全局位姿估计的新的混合方案分布。此外，我们在各种操作模式下对这些方法进行了比较，包括单目感测和基于标准里程计的方法。我们还详细研究了用于SLAM的RBPF，解决了在实现实时、稳健和数值可靠的滤波行为方面的问题。最后，我们给出了实验结果，说明了我们所提出的模型提高了准确率，以及我们实现的效率和可扩展性。
With recent advances in real-time implementations of filters for solving the simultaneous localization and mapping (SLAM) problem in the range-sensing domain, attention has shifted to implementing SLAM solutions using vision-based sensing. This paper presents and analyses different models of the Rao-Blackwellised particle filter (RBPF) for vision-based SLAM within a comprehensive application architecture. The main contributions of our work are the introduction of a new robot motion model utilizing structure from motion (SFM) methods and a novel mixture proposal distribution that combines local and global pose estimation. In addition, we compare these under a wide variety of operating modalities, including monocular sensing and the standard odometry-based methods. We also present a detailed study of the RBPF for SLAM, addressing issues in achieving real-time, robust and numerically reliable filter behavior. Finally, we present experimental results illustrating the improved accuracy of our proposed models and the efficiency and scalability of our implementation.