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

2025-07-24

AI Summary

In complex dynamic environments, long-term modeling and state prediction of semi-static features—such as moving obstacles and transient occluders—remain challenging due to their intermittent observability and non-stationary dynamics. To address this, we propose a Bayesian multi-hypothesis hybrid filtering framework. Our approach introduces dual filter chains—persistence and emergence filters—that explicitly model feature disappearance and reappearance probabilities, enabling principled incorporation of prior knowledge, multi-hypothesis inference, and online adaptive updating. Compared with conventional weighted averaging or static filtering methods, our framework achieves significantly improved future-state prediction accuracy on both synthetic and real-world robotic datasets. It demonstrates strong robustness to observation dropouts and sustained adaptability over extended temporal horizons. Experimental validation confirms its suitability for autonomous robotic systems requiring continuous operation over hours to days.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Modeling dynamics of semi-static features in robotic environments
• Predicting future states of dynamic features accurately
• Handling missing observations and multiple hypotheses robustly

Innovation

• Multi-hypothesis tracking for dynamic features
• Bayesian persistence-emergence filter mixtures
• Online adaptable prediction of future states