Better Together: Online Probabilistic Clique Change Detection in 3D Landmark-Based Maps

TL;DR

This paper introduces a probabilistic filtering method for detecting and managing changes in 3D landmark-based maps used in SLAM, addressing the static-world assumption and enabling dynamic environment adaptation.

Contribution

It presents a novel clique-based probabilistic filter that jointly estimates landmark persistence and accounts for environmental changes in 3D SLAM maps.

Findings

Successfully detects landmark changes in simulated environments

Joint estimation improves map accuracy over static assumptions

Handles dynamic and semi-static objects effectively

Abstract

Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment.