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

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

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%).
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 285 - 296
Cite this article: Iannucci, Peter A., Narula, Lakshay, Humphreys, Todd E., "Cross-Modal Localization: Using Automotive Radar for Absolute Geolocation within a Map Produced with Visible-light Imagery," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 285-296.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In