Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance

Matthew Gadd, Daniele De Martini, and Paul Newman

Abstract: This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave scanning, 360-degrees field of view radar – a commercially promising sensor poised for exploitation in mobile autonomy. We show how a rotationallyinvariant metric embedding for radar scans can be integrated into sequence-based trajectory matching systems typically applied to videos taken by visual sensors. Due to the complete horizontal field of view inherent to the radar scan formation process, we show how this off-the-shelf sequence-based trajectory matching system can be manipulated to detect place matches when the vehicle is travelling down a previously visited stretch of road in the opposite direction. We demonstrate the efficacy of the approach on 26 km of challenging urban driving taken from the largest radar-focused urban autonomy dataset released to date – showing a boost of 30 % in recall at high levels of precision over a nearest neighbour approach.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 270 - 276
Cite this article: Gadd, Matthew, De Martini, Daniele, Newman, Paul, "Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 270-276.
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