Nov 5, 2026J/K move · Enter open · S save
Thanh Nguyen Canh, Haolan Zhang, Nak Young Chong
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1211, Japan · School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1211, Japan, and also with the Department of Robotics, Hanyang University, Gyeonggi-do 15588, South Korea
Lifelong object maps must estimate whether an object remains at its previously observed location after it leaves view. Existing persistence filters rely on class-based survival priors, making them difficult to apply reliably in open-vocabulary settings. We introduce HiPer, a hierarchical persistence filter that derives persistence beliefs directly from data rather than semantic categories. For a new object, HiPer retrieves visually and geometrically similar training instances and estimates an initial survival rate from their observed histories, without requiring class labels. This estimate initializes a conjugate Beta belief over the object's survival probability, which is updated in closed form using detections and missed observations under Rosen's persistence-filter framework. When an object is re-identified at a new location, its persistence belief is transferred with the object, enabling the map to learn object-specific mobility over time. Experiments on 120 held-out simulated sequences show that HiPer consistently achieves the best performance among competing methods. Retrieval-based priors outperform CLIP-derived label priors, and object-specific online learning provides further gains. Results on 47 held-out 3RScan environments demonstrate that the proposed retrieval-based prior also outperforms state-of-the-art baselines in real-world settings.