Problem
Research questions and friction points this paper is trying to address.
visual SLAM
perceptual aliasing
perceptual variation
semantic SLAM
VPR
HuMemSLAM: Efficient Human-Inspired Semantic Place Recognition for Robust Visual SLAM
🤖 AI Summary
为解决视觉SLAM中的感知混淆和变化问题，提出模仿人类记忆与感知机制的HuMem-VPR方法，提高地点识别准确性并降低延迟。
📝 Abstract
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. Inspired by human memory and perception, we propose HuMem-VPR, which exploits the bidirectional relationship between bottom-up perceptual evidence and top-down contextual reasoning to achieve high-level place understanding. We further introduce HuMemSLAM, the integration of HuMem-VPR with ORB-SLAM3. HuMem VPR achieved the highest aggregate retrieval accuracy on the real-image benchmark, competitive accuracy on the CARLA benchmark, and approximately two to three times lower latency than the evaluated state-of-the-art VPR methods. Across the evaluated dataset families and online experiments, HuMemSLAM substantially improved integrated Recall @1 over ORB-SLAM3's native retrieval while reducing the proposals submitted to its geometric backend.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Human-inspired
Semantic Place Recognition
Bidirectional Relationship
High Retrieval Accuracy
Low Latency
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