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

TL;DR

Perpetua is a Bayesian method for modeling semi-static environmental features in robotic systems, allowing prediction of future states by tracking multiple hypotheses and incorporating prior knowledge, thus improving mapping accuracy in dynamic environments.

Contribution

The paper introduces Perpetua, a novel Bayesian approach that models feature dynamics, tracks multiple hypotheses, and predicts future states, enhancing robotic environment modeling in semi-static settings.

Findings

Perpetua outperforms similar methods in accuracy.

It is scalable and robust to missing data.

The approach works on both simulated and real-world data.

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.