19 accepted papers
This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parame…
Learning autonomous driving capabilities requires diverse and realistic training data. This has led to exploring generative techniques as an alternative to real-world data collection. In this paper we propose a method for synthesising photo-realistic urban driving scenes, along with semantic, instan…
Semantic Bird's Eye View (BEV) representations are a popular format, being easily interpretable and editable. However, synthesising ground-view images from BEVs is a difficult task as the system would need to learn both the mapping from BEV to Front View (FV) structure as well as to synthesise highl
We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in…
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic clas…
This paper adapts a general dataset representation technique to produce robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world mobile robot localisation. Two parallel lines of work on VPR have shown, on one side, that general-purpose off-the-shelf feature representations can…
Localisation with Frequency-Modulated Continuous-Wave (FMCW) radar has gained increasing interest due to its inherent resistance to challenging environments. However, complex artefacts of the radar measurement process require appropriate uncertainty estimation - to ensure the safe and reliable appli…
Selecting appropriate datasets is critical in modern computer vision. However, no general-purpose tools exist to evaluate the extent to which two datasets differ. For this, we propose representing images -- and by extension datasets -- using Distributions of Neuron Activations (DNAs). DNAs fit distr…
This work proposes a fast deployment pipeline for visually-servoed robots which does not assume anything about either the robot - e.g. sizes, colour or the presence of markers - or the deployment environment. Specifically, we apply a learning based approach to reliably estimate the pose of a robot i…
This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its s…
In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases into high quality RGB images and the sparse depth maps into pixel-wise dense depth…
Masking by Moving (MByM), provides robust and accurate radar odometry measurements through an exhaustive correlative search across discretised pose candidates. However, this dense search creates a significant computational bottleneck which hinders real-time performance when high-end GPUs are not ava…
This letter presents a method that leverages vehicle motion constraints to refine data associations in a point-based radar odometry system. By using the strong prior on how a non-holonomic robot is constrained to move smoothly through its environment, we develop the necessary framework to estimate e
In this work, a neural network is trained to simultaneously perform segmentation and pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of unknown regions of scenes can be rejected. This is made possible by leveraging an OoD dataset with a novel contrastive objective and data…
This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave scanning radar which extends the state-of-the-art by an efficient, learning-based approach to handle radar data for localisation. We learn a metric space for embedding polar radar…
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 is robust to environmental conditions such as fog, rain, snow, or lens flare…
This paper is about fast motion estimation with scanning radar. We use weak supervision to train a focus of attention policy which actively down-samples the measurement stream before data association steps are undertaken. At training, we avoid laborious manual labelling by exploiting short-term sens…
This paper is about underpinning long-term operations of fleets of vehicles using visual localisation. In particular it examines ways in which vehicles, considered as independent agents, can share, update and leverage each others' visual experiences in a mutually beneficial way. We draw on our previ…
This paper presents a universally applicable graph-based framework for the navigation of warehouse robots equipped with only monocular cameras. We strongly advocate the use of relative pose information stored in a topological map, rather than a globally consistent metric representation of the enviro…