SOURCE-LINKED INTELLIGENCE
HuMemSLAM: Efficient Human-Inspired Semantic Place Recognition for Robust Visual SLAM
Autonomous systems require reliable place recognition for efficient and effective simultaneous localisation and mapping (SLAM). Traditional geometric visual SLAM approaches rely on low-level features and geometric consistency, but remain vulnerable to perceptual aliasing, where different places appear similar, and perceptual variation, where the same place appears different. Although semantic SLAM and modern learned visual place recognition (VPR) methods improve robustness under challenging perceptual conditions, real-time deployment requires both high retrieval accuracy and low latency. Inspi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T13:32:32.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.