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Bootstrapping Autonomous Driving Radars with Self-Supervised Learning
The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather.
Benchmarking Learnt Radio Localisation under Distribution Shift
Deploying radio frequency (RF) localisation systems invariably entails non-trivial effort, particularly for the latest learning-based breeds.
Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence
Deep learning has revolutionised computer vision but has had limited application to radio perception tasks, in part due to lack of systematic datasets and benchmarks dedicated to the study of the performance and promise of radio sensing.
Self-Supervised Radio-Visual Representation Learning for 6G Sensing
In future 6G cellular networks, a joint communication and sensing protocol will allow the network to perceive the environment, opening the door for many new applications atop a unified communication-perception infrastructure.
Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport
(2) We propose neural architectures and algorithms to assimilate knowledge from an indexed set of sensor positions in order to enhance the robustness and generalisability of robotic inertial tracking in the field.
