Incremental sparse GP regression for continuous-time trajectory estimation and mapping
Incremental sparse GP regression for continuous-time trajectory estimation and mapping
复制标题
用于连续时间轨迹估计和绘图的增量稀疏 GP 回归
DOI：
10.1016/j.robot.2016.10.004
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Byron Boots
中科院分区：
文献类型：
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作者：
Xinyan Yan;V. Indelman;Byron Boots
最近在移动机器人同步轨迹估计和映射（STEAM）方面的工作使用高斯过程（GPs）通过其环境有效地表示机器人的轨迹。与离散时间轨迹表示相比，GPs有几个优点：它们可以表示连续时间轨迹，优雅地处理异步和稀疏测量，并允许机器人查询轨迹以在任何感兴趣的时间恢复其估计位置。GP方法对STEAM的一个主要缺点是它被表述为batch弹道估计问题。在本文中，我们提供了必要的关键扩展，以将现有的基于gp的批处理算法转换为非常有效的增量算法。特别是，我们能够通过有效的变量重排序和增量稀疏更新大大加快求解时间，我们相信这将大大提高高斯过程方法用于机器人映射和定位的实用性。最后，我们在合成数据集和真实数据集上展示了该方法及其优点。
Recent work on simultaneous trajectory estimation and mapping (STEAM) for mobile robots has used Gaussian processes (GPs) to efficiently represent the robot’s trajectory through its environment. GPs have several advantages over discrete-time trajectory representations: they can represent a continuous-time trajectory, elegantly handle asynchronous and sparse measurements, and allow the robot to query the trajectory to recover its estimated position at any time of interest. A major drawback of the GP approach to STEAM is that it is formulated as abatchtrajectory estimation problem. In this paper we provide the critical extensions necessary to transform the existing GP-based batch algorithm for STEAM into an extremely efficient incremental algorithm. In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization. Finally, we demonstrate the approach and its advantages on both synthetic and real datasets.