Modular Robot and Landmark Localisation Using Relative Bearing Measurements

TL;DR

This paper introduces a modular nonlinear filtering approach for robot-landmark localization that efficiently estimates states using relative bearing measurements, integrating Covariance Intersection to handle shared information and reduce communication.

Contribution

The paper presents a novel modular filtering method with Covariance Intersection integration for landmark localization, enabling independent subsystem updates and reduced communication overhead.

Findings

Modular approach performs comparably to joint filters in simulations.

Covariance Intersection effectively prevents double counting of information.

Reduced communication variants degrade gracefully in performance.

Abstract

In this paper we propose a modular nonlinear least squares filtering approach for systems composed of independent subsystems. The state and error covariance estimate of each subsystem is updated independently, even when a relative measurement simultaneously depends on the states of multiple subsystems. We integrate the Covariance Intersection (CI) algorithm as part of our solution in order to prevent double counting of information when subsystems share estimates with each other. An alternative derivation of the CI algorithm based on least squares estimation makes this integration possible. We particularise the proposed approach to the robot-landmark localization problem. In this problem, noisy measurements of the bearing angle to a stationary landmark position measured relative to the SE(2) pose of a moving robot couple the estimation problems for the robot pose and the landmark position. In a randomized simulation study, we benchmark the proposed modular method against a monolithic joint state filter to elucidate their respective trade-offs. In this study we also include variants of the proposed method that achieve a graceful degradation of performance with reduced communication and bandwidth requirements.