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kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

Department of Engineering Science, Oxford Robotics Institute, University of Oxford, Oxford OX1 3PJ, UK
Authors to whom correspondence should be addressed.
Sensors 2020, 20(21), 6002;
Received: 14 September 2020 / Revised: 16 October 2020 / Accepted: 19 October 2020 / Published: 22 October 2020
(This article belongs to the Special Issue Sensing Applications in Robotics)
This paper presents a hierarchical approach to place recognition and pose refinement for Frequency-Modulated Continuous-Wave (FMCW) scanning radar localisation.
This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions. View Full-Text
Keywords: radar; mapping; localisation; place recognition; autonomous vehicles; deep learning radar; mapping; localisation; place recognition; autonomous vehicles; deep learning
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MDPI and ACS Style

De Martini, D.; Gadd, M.; Newman, P. kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation. Sensors 2020, 20, 6002.

AMA Style

De Martini D, Gadd M, Newman P. kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation. Sensors. 2020; 20(21):6002.

Chicago/Turabian Style

De Martini, Daniele, Matthew Gadd, and Paul Newman. 2020. "kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation" Sensors 20, no. 21: 6002.

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