Mapping Extended Landmarks for Radar SLAM

TL;DR

This paper introduces a Bayesian random matrix method for estimating extended landmarks in radar SLAM, improving localization accuracy and providing more consistent environment mapping compared to traditional ellipse fitting techniques.

Contribution

It adapts the Bayesian random matrix approach for landmark extent estimation in radar SLAM, addressing sensor and measurement uncertainties.

Findings

More consistent landmark extent estimation than model-free ellipse fitting

Improved localization accuracy by exploiting landmark extent

Validated through comparative experiments

Abstract

Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the extent of landmarks is vital for precise navigation and path planning. Although object extent estimation has been successfully applied in target tracking, its adaption to SLAM remains unaddressed due to the additional uncertainty of the sensor platform, bias in the odometer reading, as well as the measurement non-linearity. In this paper, we propose to incorporate the Bayesian random matrix approach to estimate the extent of landmarks in radar SLAM. We describe the details for implementation of landmark extent initialization, prediction and update. To validate the performance of our proposed approach we compare with the model-free ellipse fitting algorithm with results showing more consistent extent estimation. We also demonstrate that exploiting the landmark extent in the state update can improve localization accuracy.