16 citations · 17 across the 3 of their papers we have counts for
4 papers
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry
Robert Weston, Matthew Gadd, Daniele De Martini +2
Masking By Moving (MByM), provides robust and accurate radar odometry measurements through an exhaustive correlative search across discretised pose candidates. However, this dense…
There and Back Again: Learning to Simulate Radar Data for Real-World Applications
Rob Weston, Oiwi Parker Jones, Ingmar Posner
Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty…
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
Dan Barnes, Rob Weston, Ingmar Posner
This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor…
Probably Unknown: Deep Inverse Sensor Modelling In Radar
Rob Weston, Sarah Cen, Paul Newman +1
Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However,…