Exactly sparse delayed-state filters for view-based SLAM
Exactly sparse delayed-state filters for view-based SLAM
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DOI：
10.1109/tro.2006.886264
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发表时间：
2006-12-01
影响因子：
7.8
通讯作者：
Leonard, John J.
中科院分区：
文献类型：
--
作者：
Eustice, Ryan M.;Singh, Hanumant;Leonard, John J.
本文提出了一种新的见解，即同时定位和地图（SLAM）的信息矩阵是完全稀疏的延迟状态的框架。这样的框架被用在基于视图的环境表示中，该环境依赖于扫描匹配的原始传感器数据来获得机器人运动相对于其先前所处的位置的虚拟观测。延迟状态信息矩阵的精确稀疏性与其他最近的基于特征的SLAM信息算法（诸如稀疏扩展信息滤波器或细连接树滤波器）形成对比，因为这些方法必须进行近似以迫使基于特征的SLAM信息矩阵稀疏。延迟状态框架的精确稀疏性的好处是，它允许人们利用信息空间参数化，而不会产生任何稀疏近似误差。因此，它可以产生与全协方差解等效的结果。实验验证的方法使用单目图像的两个数据集：地面真相的试验坦克实验，和远程操作的车辆调查的RMS泰坦尼克号。
This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.