Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition (2103.01486v1)

Abstract: 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.

• The paper introduces Patch-NetVLAD, a novel fusion of patch-level and global descriptors for enhanced visual place recognition.
• It employs multi-scale fusion and an efficient IntegralVLAD strategy, achieving up to 330% improvement in recall performance.
• Its configurability and speed-optimized design make it ideal for real-time robotics and autonomous navigation in dynamic environments.

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition

The paper introduces Patch-NetVLAD, a sophisticated method for Visual Place Recognition (VPR) to address challenges of appearance and viewpoint changes in dynamic environments. The approach innovatively combines local and global descriptor methodologies by deriving patch-level features from NetVLAD residuals, presenting a novel solution that overcomes limitations of traditional descriptors.

Methodological Contributions

Patch-NetVLAD capitalizes on the benefits of both global descriptors and local keypoint features. The methodology involves deriving patch-level descriptors from a grid in the feature space, enhancing the ability to match local features efficiently. Additionally, the system incorporates a multi-scale fusion of patch features, thereby improving robustness against differing conditions such as lighting, seasonal changes, and rotations or translations in viewpoint.

The authors further introduce an efficient computational strategy termed IntegralVLAD, analogous to integral images, enabling rapid computation of patch descriptors at multiple scales. This approach remarkably offsets the computational cost typically associated with multi-scale methods, maintaining performance agility.

Performance Analysis

Extensive evaluation on challenging datasets like Nordland, Pittsburgh, Tokyo24/7, and others highlights the superior performance of Patch-NetVLAD, which decisively outperforms state-of-the-art global and local descriptor-based methods. Notably, the method achieved a relative increase in recall performance of up to 330% compared to existing techniques. Patch-NetVLAD’s adaptability is also underscored by a high degree of configurability, where a speed-optimized version maintains effectiveness while drastically lowering computation times.

Implications and Future Directions

The implications of this work are significant for applications in robotics and autonomous systems where VPR is vital. The method’s state-of-the-art performance combined with configurability ensures utility in both pre-mapped navigation and real-time SLAM systems. Future research can explore integrating deep learning-based matchers like SuperGlue within the Patch-NetVLAD framework to further enhance performance.

Additionally, the research invites exploration in biologically inspired VPR models, leveraging the similarities in how visual information is processed via multi-scale receptive fields in human vision systems. Another promising extension could involve semantic feature analysis to filter dynamic objects, which often introduce variability in VPR tasks.

In conclusion, Patch-NetVLAD sets a new benchmark in the domain of place recognition by marrying the strengths of local and global descriptors, offering both high performance and computational flexibility, crucial for real-world applications in changing environments.