Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups
Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups
复制标题
矩阵李群连续时间轨迹估计的稀疏高斯过程
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
F. Dellaert
中科院分区：
文献类型：
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作者：
Jing Dong;Byron Boots;F. Dellaert
连续时间轨迹表示是一种强大的工具，可用于解决许多实际的同时定位和映射（SLAM）场景中的几个问题，例如连续收集的测量结果因机器人运动而失真，或者在异步传感器测量期间。稀疏高斯过程（GP）允许概率非参数轨迹表示，其使得能够通过稀疏GP回归进行快速轨迹估计。然而，以前的方法仅限于处理状态的向量空间表示。在本技术报告中，我们将Barfoot等人的工作[1]扩展到一般矩阵李群，通过应用常数速度先验，并定义局部线性GP。这使得能够在实际SLAM设置的大空间中使用稀疏GP方法。在本报告中，我们给出了理论，并在未来的出版物中留下实验评估。
Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation that enables fast trajectory estimation by sparse GP regression. However, previous approaches are limited to dealing with vector space representations of state only. In this technical report we extend the work by Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity prior, and defining locally linear GP. This enables using sparse GP approach in a large space of practical SLAM settings. In this report we give the theory and leave the experimental evaluation in future publications.
影响因子：
18.6
作者：
Hertzberg, Christoph;Wagner, Rene;Schroeder, Lutz
通讯作者：
Schroeder, Lutz