Probabilistic Object Maps for Long-Term Robot Localization

TL;DR

This paper introduces probabilistic object maps (POMs) that model movable objects' distributions to enhance long-term robot localization in dynamic environments, demonstrating improved accuracy and robustness.

Contribution

The paper proposes a novel probabilistic object mapping approach and a localization method that leverages these maps for better long-term robot localization.

Findings

POM-Localization achieves globally consistent localization in real-world environments.

POM-Localization improves trajectory accuracy even with partially incorrect data.

The approach effectively models movable objects to handle environmental changes.

Abstract

Robots deployed in settings such as warehouses and parking lots must cope with frequent and substantial changes when localizing in their environments. While many previous localization and mapping algorithms have explored methods of identifying and focusing on long-term features to handle change in such environments, we propose a different approach -- can a robot understand the distribution of movable objects and relate it to observations of such objects to reason about global localization? In this paper, we present probabilistic object maps (POMs), which represent the distributions of movable objects using pose-likelihood sample pairs derived from prior trajectories through the environment and use a Gaussian process classifier to generate the likelihood of an object at a query pose. We also introduce POM-Localization, which uses an observation model based on POMs to perform inference on a factor graph for globally consistent long-term localization. We present empirical results showing that POM-Localization is indeed effective at producing globally consistent localization estimates in challenging real-world environments and that POM-Localization improves trajectory estimates even when the POM is formed from partially incorrect data.