用概率图与多世界决策提升视觉导航的不确定性应对能力
PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making
- 构建包含语义分布的3D概率场景图，量化感知不确定性
- 通过多世界采样评估多个可能世界中的最优导航点
- 在线校准检测结果，适合长时学习的开放词汇导航任务
开放词汇导航要求智能体应对由语义模糊和模型误差引起的显著感知不确定性。然而，现有方法多采用局部最优的确定性策略，忽略了对多重复合可能性的全局决策。本文提出概率场景图导航（PSG-Nav），构建3D概率场景图，使用完整的语义类别分布来建模感知不确定性。为高效利用局部分布组合并推理最优导航地标，提出多世界决策机制，从联合分布中采样多个最可能的世界设定，并基于地标与多世界间的兼容性评估导航选择。为缓解开放词汇导航中因认知不确定性导致的误报，引入证据经验校准器，通过交叉验证当前检测与过往成功/失败记忆，实现在线终身适应。在广泛使用的MP3D、HM3D和HSSD基准上，PSG-Nav取得新最优表现，成功率分别为66.1%、44.8%和67.9%。
原文摘要 · Abstract (English)
Open-vocabulary navigation requires embodied agents to manage significant perception uncertainty stemming from semantic ambiguity and model errors. However, most existing works settle for local optimal deterministic approaches, depriving complex navigation decision-making over multiple composite possibilities that are critical for globally better solutions. In this paper, we propose Probabilistic Scene Graph Navigation (PSG-Nav), which constructs a 3D Probabilistic Scene Graph that uses full semantic categorical distributions to account for perception uncertainty. To efficiently use the local distributions to compose and reason about the optimal navigation landmarks, we propose Multiverse Decision to sample multiple most likely world settings from the joint distribution, and evaluate navigation landmarks based on the compatibility between landmarks and multiverses. To mitigate false positives due to epistemic uncertainty in open-vocabulary navigation, we introduce the Evidential Experience Calibrator, which enables online lifelong adaptation by cross-validating detections against memories of past successes and failures. Extensive experiments on widely-used benchmarks MP3D, HM3D, and HSSD demonstrate that PSG-Nav establishes new state-of-the-art results, achieving Success Rates of 66.1%, 44.8%, and 67.9%, respectively. Code is available at: https://psg-nav.github.io/
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