顺序位置学习：无启发式高性能长期位置识别
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
摘要：
近十年来，使用手工制作的序列分析的顺序匹配一直是基于路线的位置识别的标准实践，用于增强成对相似性结果。然而，这些算法的精度召回性能显着下降时，搜索短时间窗口（TW）的长度，同时要求高的计算和存储成本的大型机器人数据集的自主导航研究。在这里，受生物系统的影响，即使没有视觉，也能稳健地导航时空尺度，我们通过顺序过程开发了一种联合视觉和位置表示学习技术，并设计了一种基于学习的CNN+LSTM架构，可通过时间反向传播进行训练，用于视点和外观不变的位置识别。我们的方法，顺序位置学习（SPL），是基于一个CNN函数，从一个单一的遍历视觉编码的环境，从而减少存储容量，而LSTM时间融合每个视觉嵌入与相应的位置数据-从任何来源的运动估计-直接顺序推理。与经典的两级流水线相反，例如，match-then-temporally-filter，我们的网络直接消除了误报率，同时联合学习从单个单目图像序列进行序列匹配，即使使用短TW。因此，我们证明了我们的模型优于15种经典方法，同时在4个具有挑战性的基准数据集上设置了新的最先进的性能标准，其中一个可以被认为以100%的召回率和100%的精度解决，正确匹配极端阳光-黑暗变化下的所有地方。此外，我们还表明，在729公里的路线上，SPL的部署速度比经典方法快70倍，其中包括35，768个连续帧。大量的实验表明，…基线代码可在https://github.com/mchancan/deepseqslam上获得
英文摘要：
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these algorithms dramatically degrades when searching on short temporal window (TW) lengths, while demanding high compute and storage costs on large robotic datasets for autonomous navigation research. Here, influenced by biological systems that robustly navigate spacetime scales even without vision, we develop a joint visual and positional representation learning technique, via a sequential process, and design a learning-based CNN+LSTM architecture, trainable via backpropagation through time, for viewpoint- and appearance-invariant place recognition. Our approach, Sequential Place Learning (SPL), is based on a CNN function that visually encodes an environment from a single traversal, thus reducing storage capacity, while an LSTM temporally fuses each visual embedding with corresponding positional data -- obtained from any source of motion estimation -- for direct sequential inference. Contrary to classical two-stage pipelines, e.g., match-then-temporally-filter, our network directly eliminates false-positive rates while jointly learning sequence matching from a single monocular image sequence, even using short TWs. Hence, we demonstrate that our model outperforms 15 classical methods while setting new state-of-the-art performance standards on 4 challenging benchmark datasets, where one of them can be considered solved with recall rates of 100% at 100% precision, correctly matching all places under extreme sunlight-darkness changes. In addition, we show that SPL can be up to 70x faster to deploy than classical methods on a 729 km route comprising 35,768 consecutive frames. Extensive experiments demonstrate the... Baseline code available at https://github.com/mchancan/deepseqslam
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