让视觉定位模型学会判断自身置信度，提升机器人导航安全性
KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision
- 用原型锚定监督学习，将特征表示为球面分布预测不确定性
- 在5个数据集上将校准误差降低50%，召回率不降反升
- 可适配现有模型，适合需要可靠定位决策的机器人系统
视觉定位（VPR）对自主导航至关重要，但现有方法缺乏可靠的不确定性估计。标准流程无法有效提示查询模糊或匹配可能错误，危及安全关键型机器人应用。本文提出KappaPlace，一种基于原型锚定监督的不确定性感知VPR框架。核心是将潜在类别代表作为目标，采用概率性损失函数。通过将图像描述符建模为冯·米塞斯-费舍尔（vMF）分布，设计轻量模块直接预测集中参数以表征随机不确定性。突破传统仅从查询视角出发的局限，提出新的匹配级不确定性量化方式。在五个不同基准测试中，相较于现有方法，KappaPlace将期望校准误差（ECE@K）降低最高达50%，同时保持或提升检索召回率。提供联合训练与冻结主干的后处理两种实现方式。结果表明，KappaPlace能提供稳定、可靠的不确定性信号，支持更安全的决策。代码已开源。
原文摘要 · Abstract (English)
Visual Place Recognition (VPR) is critical for autonomous navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standard pipelines cannot reliably signal when a query is ambiguous or a match is likely incorrect, posing risks in safety-critical robotics. We propose KappaPlace, a principled framework for learning uncertainty-aware VPR representations. Our core contribution is a Prototype-Anchored supervision strategy that leverages latent class representatives as targets for a probabilistic objective. By modeling image descriptors as von Mises-Fisher (vMF) variables, we learn a lightweight module to predict the concentration parameter as a direct proxy for aleatoric uncertainty. While existing VPR uncertainty methods are typically restricted to a query-centric view, we derive a novel match-level formulation to quantify the reliability of specific query-reference pairs. Across five diverse benchmarks, KappaPlace reduces Expected Calibration Error (ECE@K) by up to 50% compared to existing methods while maintaining or improving retrieval recall. We provide both a joint-training variant and a post-training extension for frozen backbones. Our results demonstrate that KappaPlace provides a robust, stable, and well-calibrated signal that enables reliable decision-making within the VPR pipeline. Our code is available at: https://github.com/mayayank95/UncertaintyAwareVPR
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。