An Online Sparsity-Cognizant Loop-Closure Algorithm for Visual Navigation
An Online Sparsity-Cognizant Loop-Closure Algorithm for Visual Navigation
复制标题
一种用于视觉导航的在线稀疏性认知闭环算法
DOI：
10.15607/rss.2014.x.036
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
José Neira
中科院分区：
文献类型：
--
作者：
Y. Latif;G. Huang;J. Leonard;José Neira
对于机器人来说，能够检测重访或环路闭合对于长期视觉导航至关重要。一个关键的见解是，闭环事件本质上是稀疏发生的，即，当前拍摄的图像仅与先前观察的一个小子集（如果有的话）匹配。基于这一观察，我们制定了一个稀疏的，凸的1-最小化问题的循环关闭检测的问题。通过利用快速凸优化技术，我们能够有效地找到循环闭包，从而实现实时机器人导航。这种新的配方不需要离线字典学习，所需的大多数现有的方法，从而允许在线增量操作。我们的方法确保了一个全球性的，唯一的假设，只选择一个单一的全球最佳匹配时，作出闭环决策。此外，所提出的公式享有灵活的表示，没有对图像应该如何表示施加限制，同时仅要求当对应的图像在视觉上相似时表示彼此接近。使用公共真实世界数据集对所提出的算法进行了广泛验证。
It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation. A key insight is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous observations. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex `1-minimization problem. By leveraging on fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a global, unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation, with no restriction imposed on how images should be represented, while requiring only that the representations be close to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using public real-world datasets.