Next Article in Journal
A 5.86 Million Quality Factor Cylindrical Resonator with Improved Structural Design Based on Thermoelastic Dissipation Analysis
Previous Article in Journal
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder
Previous Article in Special Issue
Applying a 6 DoF Robotic Arm and Digital Twin to Automate Fan-Blade Reconditioning for Aerospace Maintenance, Repair, and Overhaul
Open AccessArticle

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; https://doi.org/10.3390/s20216002
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
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop