# Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments

arXiv: https://arxiv.org/abs/2507.18808 · HTML: https://mlsanity.com/paper/2507.18808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency](https://mlsanity.com/paper/2109.10165/) (2021)
- [Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments](https://mlsanity.com/paper/2402.13817/) (2024)
- [3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs](https://mlsanity.com/paper/2209.07896/) (2022)
- [NeuSE: Neural SE(3)-Equivariant Embedding for Long-Term Object-Based Simultaneous Localization and Mapping](https://mlsanity.com/paper/2303.07308/) (2023)
- [Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes](https://mlsanity.com/paper/1909.12733/) (2019)
- [Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment](https://mlsanity.com/paper/1912.02149/) (2019)
- [The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation](https://mlsanity.com/paper/2208.12602/) (2022)
- [Probabilistic Object Maps for Long-Term Robot Localization](https://mlsanity.com/paper/2110.00128/) (2021)
- [Curating Long-term Vector Maps](https://mlsanity.com/paper/2007.15736/) (2020)
- [Warped Hypertime Representations for Long-term Autonomy of Mobile Robots](https://mlsanity.com/paper/1810.04285/) (2018)
- [Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments](https://mlsanity.com/paper/2404.14546/) (2024)

## Built on by (most cited first, 2 of 2)

- [PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes](https://mlsanity.com/paper/2607.04057/) (2026)
- [Retrospective Open-Vocabulary Memory for Long-Term Object Search](https://mlsanity.com/paper/2610.00330/) (2026)

## Abstract (arXiv)

Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of "persistence" and "emergence" filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.