基于图核的鲁棒视觉位置识别

E. Stumm, Christopher Mei, S. Lacroix, Juan I. Nieto, M. Hutter, R. Siegwart
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引用次数: 47

摘要

介绍了一种新的视觉位置识别方法,并对其进行了评估,证明了该方法对感知混叠和观测噪声的鲁棒性。这是通过更加结构化的视觉观察来增加歧视来实现的。观测似然的估计是基于图核公式,利用结构和视觉信息编码在共可见性图。所提出的概率模型能够通过利用视觉观测中的信息来规避典型的困难和昂贵的后验归一化过程。此外,地点识别的复杂度与地图的大小无关。结果表明,在不同的公共数据集和新实验集上,该方法比现有方法有所改进,突出了该方法的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Visual Place Recognition with Graph Kernels
A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are based on graph kernel formulations, utilizing both the structural and visual information encoded in covisibility graphs. The proposed probabilistic model is able to circumvent the typically difficult and expensive posterior normalization procedure by exploiting the information available in visual observations. Furthermore, the place recognition complexity is independent of the size of the map. Results show improvements over the state-of-theart on a diverse set of both public datasets and novel experiments, highlighting the benefit of the approach.
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