51 citations · 97 across the 5 of their papers we have counts for
6 papers
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
Will Maddern, Geoffrey Pascoe, Matthew Gadd +3
We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset. The release includes…
Distant Vehicle Detection Using Radar and Vision
Simon Chadwick, Will Maddern, Paul Newman
For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speed…
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
Horia Porav, Will Maddern, Paul Newman
We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the c…
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
Dan Barnes, Will Maddern, Geoffrey Pascoe +1
We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage…
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Torsten Sattler, Will Maddern, Carl Toft +9
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization a…
Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy
Dan Barnes, Will Maddern, Ingmar Posner
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes fro…