AI

This is a category for all research topics / papers done within A2I.
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Learning Robust, Distraction-Free Radar Odometry from Pose Information

Masking by Moving: Learning Robust, Distraction-Free Radar Odometry from Pose Information Abstract - 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 objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of [...]

Learning Robust, Distraction-Free Radar Odometry from Pose Information2019-09-26T18:46:10+01:00

The Oxford Radar RobotCar Dataset

The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset Abstract - In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data. The target application is autonomous vehicles where this modality remains unencumbered by environmental conditions such as fog, [...]

The Oxford Radar RobotCar Dataset2019-09-26T13:13:22+01:00

Deep Inverse Sensor Modelling in Radar

In the last decade, systems utilising camera and lasers have been remarkably successful increasing our expectations for what robotics might achieve in the decade to come. Our robots now need to see further, not only operating in environments where humans can operate, but also in environments where humans cannot! To this end radar is a [...]

Deep Inverse Sensor Modelling in Radar2019-04-17T15:55:44+01:00

On the Limitations of Representing Functions on Sets

Our recent work on analysing a set of permutation invariant neural network architectures is probably on the theoretical end of the spectrum of the type of work we do at the A2I lab. Nevertheless it is equally exciting as it has concrete implications for real-world robotics such as working with point clouds from Lidars. [...]

On the Limitations of Representing Functions on Sets2019-03-27T15:10:25+01:00

Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments Abstract – 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 offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each [...]

Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments2019-03-27T15:01:57+01:00

Hierarchical Attentive Recurrent Tracking

Hierarchical Attentive Recurrent Tracking Abstract – Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate “where” and “what” processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model [...]

Hierarchical Attentive Recurrent Tracking2019-03-27T15:03:35+01:00

Mutual Alignment Transfer Learning from Simulation to the Real World

Abstract - Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, these can [...]

Mutual Alignment Transfer Learning from Simulation to the Real World2017-09-18T22:35:47+01:00

Adversarial Domain Adaptation

Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While the model is optimised for the training domain it will deliver degraded performance in application domains that underlie distributional shifts [...]

Adversarial Domain Adaptation2017-09-18T22:39:46+01:00

Probabilistic Prediction of Perception Performance

Learn from Experience: Probabilistic Prediction of Perception Performance to Avoid Failure Abstract –Despite the significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging, time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their [...]

Probabilistic Prediction of Perception Performance2017-05-09T12:09:30+01:00

Deep Inverse Reinforcement Learning

Large-Scale Cost Function Learning for Path Planning using Deep Inverse Reinforcement Learning Abstract - We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of [...]

Deep Inverse Reinforcement Learning2017-09-18T22:40:27+01:00