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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

Deep Tracking

Deep tracking in the wild: End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks Abstract – This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches to tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict [...]

Deep Tracking2017-09-18T22:40:11+01:00

Weakly-Supervised Path Proposals for Urban Autonomy

Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy Abstract – 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 from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths [...]

Weakly-Supervised Path Proposals for Urban Autonomy2018-12-10T12:40:48+01:00

Auditory perception

Abstract - Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we [...]

Auditory perception2017-09-18T22:42:03+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 Performance2017-09-18T22:42:07+01:00

Efficient Object Detection from 3D Point Clouds

Learning Sparse Representations with CNNs for Efficient Object Detection in 3D Point Clouds Abstract – Convolutional neural networks (CNNs) have exhibited state-of-the-art performance across a number of domains, but have yet to realise the same success when applied to 3D point cloud data. This is in part due to the third spatial dimension, which renders the [...]

Efficient Object Detection from 3D Point Clouds2017-09-18T22:41:32+01:00

Building, Curating, and Querying Large-scale Data Repositories for Field Robotics Applications

Abstract—Field robotics applications have some unique and unusual data requirements -- the curating, organisation and management of which are often overlooked. An emerging theme is the use of large corpora of spatiotemporally indexed sensor data which must be searched and leveraged both offline and online. Increasingly we build systems that must never stop learning. Every [...]

Building, Curating, and Querying Large-scale Data Repositories for Field Robotics Applications2016-10-22T19:49:34+01:00

From Dusk till Dawn: Localisation at Night using Artificial Light Sources

Abstract—This paper is about localising at night in urban environments using vision. Despite it being dark exactly half of the time, surprisingly little attention has been given to this problem. A defining aspect of night-time urban scenes is the presence and effect of artificial lighting -- be that in the form of street or interior lighting through windows. By [...]

From Dusk till Dawn: Localisation at Night using Artificial Light Sources2016-10-22T19:49:34+01:00

Work Smart, Not Hard: Recalling Relevant Experiences for Vast-Scale but Time-Constrained Localisation

This paper is about life-long vast-scale localisation in spite of changes in weather, lighting and scene structure. Building upon our previous work in Experience-based Navigation, we continually grow and curate a visual map of the world that explicitly supports multiple representations of the same place. We refer to these representations as experiences, where a single [...]

Work Smart, Not Hard: Recalling Relevant Experiences for Vast-Scale but Time-Constrained Localisation2016-10-22T19:49:34+01:00

Leveraging Experience for Long-Term LIDAR Localisation In Changing Cities

 Successful approaches to autonomous vehicle localisation and navigation typically involve 3D LIDAR scanners and a static, curated 3D map, both of which are expensive to acquire and maintain. We propose an experience-based approach to matching a local 3D swathe built using a push-broom 2D LIDAR to a number of prior 3D maps, each of which [...]

Leveraging Experience for Long-Term LIDAR Localisation In Changing Cities2018-06-20T15:10:08+01:00