.png){kind=icon}
返回
Dilated Superpixel Aggregation for Visual Place Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/LRA.2025.3645658.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Image segmentation
Feature extraction
Visual place recognition
Robustness
Image color analysis
Visualization
Foundation models
Computational modeling
Accuracy
Vectors
Localization
vision-based navigation
visual place recognition (VPR)
superpixel
aggregation
期刊
I
IF:
5.3
论文数:
1.9K
被引数:
3.9W
机构
引用论文
.png){kind=icon}
分享收藏
A Hybrid Approach Incorporating Superpixels for Diabetic Foot Lesion Segmentation Using YOLOv5 and SAM结合超像素的糖尿病足病变分割混合方法，使用YOLOv5和SAM
PREAI
分享收藏A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition
OAAI
OAAI分享收藏
查看更多内容