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Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision

Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision Abstract – 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 perception systems deteriorates significantly if certain environmental factors are present e.g. rain, fog, darkness. Perception [...]

Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision2020-05-11T16:54:01+01:00

Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios

Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios Abstract – This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, [...]

Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios2020-05-26T14:38:48+01:00

RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar

RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar Abstract – This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using FMCW scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more [...]

RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar2020-04-07T12:14:04+01:00

LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic

LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisible to the sensor (due to occlusion, e.g. traffic) but can be located by our Occluded Road Boundary Inference Deep [...]

LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic2020-03-10T17:57:34+00:00

Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance

Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance Abstract - This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave radar - a commercially promising sensor poised for exploitation in mobile autonomy. We show how a rotationally-invariant metric embedding for radar scans can [...]

Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance2020-03-10T17:54:45+00:00

Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset

Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset Abstract - 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 72 traversals of a route through Oxford, UK, gathered in all illumination, weather and traffic conditions, and is [...]

Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset2020-02-25T10:31:16+00:00

Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning

Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning Abstract - This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated ContinuousWave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction [...]

Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning2020-01-30T12:33:15+00:00

I Can See Clearly Now : Image Restoration via De-Raining

This blog post provides an overview of our paper: [bibtex key="ICRA19_porav"] Abstract We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks. We introduce a novel stereo dataset recorded using a system that allows one lens to be affected by real water droplets while keeping the other lens [...]

I Can See Clearly Now : Image Restoration via De-Raining2019-11-22T12:22:23+00:00

Radar-only ego-motion estimation in difficult settings via graph matching

Radar-only ego-motion estimation in difficult settings via graph matching Abstract - Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and [...]

Radar-only ego-motion estimation in difficult settings via graph matching2020-02-12T13:51:12+00:00