5 citations · 9 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini +2
Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Alth…
Self-Supervised Localisation between Range Sensors and Overhead Imagery
Tim Y. Tang, Daniele De Martini, Shangzhe Wu +1
Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable. However, satellite images are n…
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
David Williams, Daniele De Martini, Matthew Gadd +2
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment. The performance of LiDAR and vision-based perc…
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
Matthew Gadd, Daniele De Martini, Paul Newman
This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave radar - a commercially promis…
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning
Ştefan Săftescu, Matthew Gadd, Daniele De Martini +2
This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding…