【泡泡一分钟】基于优化的视觉惯性状态估计中规范自由度处理方法的比较

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【泡泡一分钟】基于优化的视觉惯性状态估计中规范自由度处理方法的比较

2018 年 12 月 19 日 泡泡机器人SLAM

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标题：On the Comparison of Gauge Freedom Handling in Optimization-based Visual-Inertial State Estimation

作者：Zichao Zhang, Guillermo Gallego, Davide Scaramuzza

来源：ICCV 2017 ( IEEE International Conference on Computer Vision)

编译：黄思宇

审核：颜青松陈世浪

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摘要

众所周知，视觉惯性状态估计可以估计四自由度（DoF）的变换（基于重力方向的旋转和平移），同时需要正确处理额外的规范自由度。虽然在实践中已经使用了不同的处理规范自由度的方法，但是在此之前没有针对这些方法的差异进行系统分析的研究。本文首次提出了一种在基于优化的视觉-惯性状态估计框架中对规范自由度进行不同处理方式的比较分析方法。我们通过实验比较了三种常用方法：将不可观察的状态固定为给定值，对不可观察的状态设置先验，或者让不可观察的状态在优化期间自由演化。

具体来说，我们的结果表明：（i）以上三种方法的准确性和计算时间相似，其中自由规范方法略快; （ii）自由规范方法的协方差估计表面上看与其他方法完全不同，但实际上与其他方法密切相关。

我们的研究结果在仿真和实际数据集中都得到了验证，可用于设计基于优化的视觉惯性估计算法。

Abstract

It is well known that visual-inertial state estimation is possible up to a four degrees-of-freedom (DoF) transformation (rotation around gravity and translation), and the extra DoFs (“gauge freedom”) have to be handled properly. While different approaches for handling the gauge freedom have been used in practice, no previous study has been carried out to systematically analyze their differences. In this paper, we present the first comparative analysis of different methods for handling the gauge freedom in optimization-based visual-inertial state estimation. We experimentally compare three commonly used approaches: fixing the unobservable states to some given values, setting a prior on such states, or letting the states evolve freely during optimization. Specifically, we show that (i) the accuracy and computational time of the three methods are similar, with the free gauge approach being slightly faster; (ii) the covariance estimation from the free gauge approach appears dramatically different, but is actually tightly related to the other approaches. Our findings are validated both in simulation and on real-world datasets and can be useful for designing optimization-based visual-inertial state estimation algorithms.

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