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Visual Place Recognition by spatial matching of high-level CNN features
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.robot.2020.103625
Luis G. Camara , Libor Přeučil

Abstract We present a Visual Place Recognition (VPR) pipeline that achieves substantially improved precision as compared with approaches commonly appearing in the literature. It is based on a standard image retrieval configuration, with an initial stage that retrieves the closest candidates to a query from a database and a second stage where the list of candidates is re-ranked. The latter is realized by the introduction of a novel geometric verification procedure that uses the activations of a pre-trained convolutional neural network. It is both remarkably simple and robust to viewpoint and condition changes. As a stand-alone, general spatial matching methodology, it could be easily added and used to enhance existing VPR approaches whose output is a ranked list of candidates. The proposed two-stage pipeline is also improved through extensive optimization of hyperparameters and by the implementation of a frame-based temporal filter that takes into account past recognition results.

中文翻译:

通过高级 CNN 特征的空间匹配进行视觉位置识别

摘要 我们提出了一种视觉位置识别 (VPR) 管道,与文献中常见的方法相比,它实现了显着提高的精度。它基于标准图像检索配置,初始阶段从数据库中检索与查询最接近的候选者,第二阶段对候选者列表进行重新排序。后者是通过引入一种新的几何验证程序来实现的,该程序使用预训练的卷积神经网络的激活。对于视点和条件的变化来说,它既非常简单又健壮。作为一种独立的通用空间匹配方法,它可以轻松添加并用于增强现有的 VPR 方法,其输出是候选者的排名列表。
更新日期:2020-11-01
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