This paper focuses on loop-closure detection (LCD) for a visual simultaneous localization and mapping (SLAM) system. We present a strategy that combines a Bayes filter and features from a pre-trained convolution neural network (CNN) to perform LCD. Rather than using features from only one layer, we fuse features from multiple layers based on spatial pyramid pooling. A flexible Bayes model is then formulated to integrate the sequential information and similarities that are computed by features at different scales. The introduction of a penalty factor and bidirectional propagation enables our approach to handle complex trajectories. We present extensive experiments on challenging datasets, and we compare our approach to state-of-the-art methods, to evaluate it. The results show that our approach can ensure remarkable performance under severe condition changes and handle trajectories that have different characteristics. We also show the advantages of Bayes filters over sequence matching in the experiments, and we analyze our feature fusion strategy by visualizing the activations of the CNN.

中文翻译：

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使用贝叶斯滤波器和 CNN 特征的鲁棒循环闭合检测

本文重点研究视觉同步定位和映射 (SLAM) 系统的闭环检测 (LCD)。我们提出了一种结合贝叶斯滤波器和来自预训练卷积神经网络 (CNN) 的特征来执行 LCD 的策略。我们不是只使用一层的特征，而是基于空间金字塔池化融合多层的特征。然后制定一个灵活的贝叶斯模型来整合由不同尺度的特征计算的序列信息和相似性。惩罚因子和双向传播的引入使我们的方法能够处理复杂的轨迹。我们对具有挑战性的数据集进行了广泛的实验，并将我们的方法与最先进的方法进行比较，以对其进行评估。结果表明，我们的方法可以确保在严重条件变化下的卓越性能，并处理具有不同特征的轨迹。我们还在实验中展示了贝叶斯滤波器相对于序列匹配的优势，并通过可视化 CNN 的激活来分析我们的特征融合策略。