Cross-Modal Localization: Using Automotive Radar for Absolute Geolocation within a Map Produced with Visible-Light Imagery

Date

2020

Authors

Iannucci, Peter A.
Narula, Lakshay
Humphreys, Todd E.

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Abstract

This paper explores the possibility of localizing an automotive-radar-equipped vehicle within an urban environment relative to an existing map of the environment created using data from visible light cameras. Such cross-modal localization would enable robust, low-cost absolute localization in poor weather conditions based only on radar even when the vehicle has never previously visited the area. This is because a pre-existing absolutely-referenced visible-light-based map (e.g., constructed from Google Street View images) could be exploited for localization provided that a correspondence between features in this map and the vehicle’s radar returns can be established. The greatest challenge presented by cross-modal localization with automotive radar is the extreme sparseness of automotive-radar-produced features, which prevents application of standard computer vision techniques for the cross-modal registration. To the best of the authors’ knowledge, cross-modal localization using automotive- grade radar within a visible-light-based map is unprecedented. The current paper demonstrates that it can be used for vehicle localization with horizontal errors below 61 cm (95%).

Description

LCSH Subject Headings

Citation

Peter A. Iannucci, Lakshay Narula, and Todd E. Humphreys, "Cross-Modal Localization: Using Automotive Radar for Absolute Geolocation within a Map Produced with Visible-Light Imagery," In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 285-296. IEEE, 2020.