Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introduce a multi-scale fusion of patch features that have complementary scales (i.e. patch sizes) via an integral feature space and show that the fused features are highly invariant to both condition (season, structure, and illumination) and viewpoint (translation and rotation) changes. Patch-NetVLAD outperforms both global and local feature descriptor-based methods with comparable compute, achieving state-of-the-art visual place recognition results on a range of challenging real-world datasets, including winning the Facebook Mapillary Visual Place Recognition Challenge at ECCV2020. It is also adaptable to user requirements, with a speed-optimised version operating over an order of magnitude faster than the state-of-the-art. By combining superior performance with improved computational efficiency in a configurable framework, Patch-NetVLAD is well suited to enhance both stand-alone place recognition capabilities and the overall performance of SLAM systems.
翻译:对机器人和自主系统而言,视觉定位识别是一项具有挑战性的任务,它必须处理在不断变化的世界中出现外观和观点变化的双重问题。本文介绍Patch-NetVLAD,它通过从 NetVLAD 残留物中引出补丁级特征,将本地和全球描述器方法的优势结合起来。与现有本地关键点特征的固定空间周边系统不同,我们的方法使得在地格空间网格上定义的深层次地方特征能够聚合和匹配。我们进一步引入了多尺度的补丁功能组合,通过一个整体特征空间具有互补规模(即补丁大小),并表明结合的功能对于两种条件(季节性、结构、照明)和观点(翻译和旋转)都极不易变。Patch-NetVLAD将基于全球和本地特征描述器的方法与可比的兼容,在一系列具有挑战性的真实世界数据集中取得了州际的视觉识别结果,包括赢得ECCV20版的Facebook可视化位置识别度挑战性能度挑战,并显示这些功能高度不易变异性,同时将快速地将用户性业绩和更高水平的升级的计算,同时将SLALA-L-L-S-L-C-2020的计算系统升级的运行能力与一个比高级性能升级的升级的系统升级的升级的系统升级到更高性能比升级到更高性能。