Dilated Superpixel Aggregation for Visual Place Recognition

Zichao Zeng, June Moh Goo, Jan Boehm

Abstract: Visual Place Recognition (VPR) is a fundamental task in robotics and computer vision, enabling systems to identify locations seen in the past using visual information. Previous state-of-the-art approach focuses on encoding and retrieving semantically meaningful supersegment representations of images to significantly enhance recognition recall rates. However, we find that they struggle to cope with significant variations in viewpoint and scale, as well as scenes with sparse or limited information. Furthermore, these semantic-driven supersegment representations often exclude semantically meaningless yet valuable pixel information. In this work, we present Sel-V and MuSSel-V, two efficient variants within the segment-level VPR paradigm that replace heavy and fragmented supersegments with lightweight, visually compact and complete dilated superpixels for local feature aggregation. The use of superpixels preserves pixel-level details while reducing computational overhead. A multi-scale extension further enhances robustness to viewpoint and scale changes. Comprehensive experiments on twelve public benchmarks show that our approach achieves a better trade-off between accuracy and efficiency than existing segment-based methods. These results demonstrate that lightweight, non-semantic segmentation can serve as an effective alternative for high-performance, resource efficient visual place recognition in robotics.