Accurate localisation everywhere, everytime
Working in partnership with research teams we support the successful integration and reliable performance from our sensors, today and in the future. It’s this uncompromising drive for excellence in performance and service that makes us a world-leader and a team you can count on.
Self-Supervised Localisation between Range Sensors and Overhead Imagery
Tim Y. Tang, Daniele De Martini, Shangzhe Wu and Paul Newman, Robotics: Science and Systems (RSS) 2020
[Read more] [Bibtex]
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning [Read more]
Stefan Saftescu, Matthew Gadd, Daniele De Martini, Dan Barnes and Paul Newman, International Conference on Robotics and Automation (ICRA) 2020 [Watch the video] [Bibtex]
RSL-Net: Localising in Satellite Images From a Radar on the Ground
Tim Y. Tang, Daniele De Martini, Dan Barnes and Paul Newman, International Conference on Robotics and Automation (ICRA) 2020 [Read more] [Bibtex]
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar [Read more]
Dan Barnes and Ingmar Posner, International Conference on Robotics and Automation (ICRA) 2020 [Watch the video] [Bibtex]
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information [Read more]
Dan Barnes, Rob Weston and Ingmar Posner, Conference on Robot Learning (CoRL) 2019 [Watch the video] [Bibtex]
What Could Go Wrong? Introspective Radar Odometry in Challenging Environments
Roberto Aldera, Daniele De Martini, Matthew Gadd, and Paul Newman, Intelligent Transportation Systems (ITSC) Conference 2019 [Read more] [Bibtex]
Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning
Keenan Burnett, David J. Yoon, Angela P. Schoellig, and Timothy D. Barfoot, Institute for Aerospace Studies, University of Toronto 2021 [Read more] [Bibtex]
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
Matthew Gadd, Daniele De Martini, and Paul Newman, IEEE/ION Position, Location and Navigation Symposium (PLANS) 2020 [Read more] [Bibtex]
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
Prannay Kaul, Daniele De Martini, Matthew Gadd, and Paul Newman, IEEE Intelligent Vehicles Symposium (IV) 2020 [Read more] [Bibtex]
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
David Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, and Paul Newman, Intelligent Transportation Systems (ITSC) Conference 2020
[Read more] [Bibtex]
Trusted to perform
Delivering sensors since 1999, our industrial-grade, high-performance radars are consistently reliable, and designed to be maintenance free for 10 years.
We rigorously test the performance of every radar to withstand all weather, light and environmental conditions, including rain, fog, dust or dirt and complete darkness. It means no costly disruption to your operations. Sensors you can rely on.
"We are delighted to be working so closely with Navtech Radar - it speaks to the strength of the tech industry in Oxfordshire and the UK. The partnering will accelerate the art of the possible in what machines can do with radar."
Professor Paul Newman, Director Oxford Robotics Institute (ORI), a world-leading robotics research group
Work with us
Whether you are new to radar or replacing existing sensors, our technical team will support you to make the most of our radar technology.