Search Results for author:
Found 38 papers, 3 papers with code
DENSER Cities: A System for Dense Efficient Reconstructions of Cities
The computational and memory requirements of large dense models can be prohibitive.
Geometric Multi-Model Fitting with a Convex Relaxation Algorithm
Unlike greedy methods --which maximise the number of inliers-- this approach efficiently searches for a soft assignment of points to models by minimising the energy of the overall classification.
NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance
We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric.
Meshed Up: Learnt Error Correction in 3D Reconstructions
We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction.
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
We present a method of improving visual place recognition and metric localisation under very strong appear- ance change.
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source localisation in an urban scenario.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view.
I Can See Clearly Now : Image Restoration via De-Raining
We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks.
Imminent Collision Mitigation with Reinforcement Learning and Vision
This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent.
Distant Vehicle Detection Using Radar and Vision
Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles.
Robotics
Radar-only ego-motion estimation in difficult settings via graph matching
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.
Training Object Detectors With Noisy Data
The availability of a large quantity of labelled training data is crucial for the training of modern object detectors.
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving.
Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR
Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.
Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation
Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i. e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change.
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data.
Robotics Signal Processing
Learning Geometrically Consistent Mesh Corrections
In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes.
RSL-Net: Localising in Satellite Images From a Radar on the Ground
This paper is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle.
Learning to Correct 3D Reconstructions from Multiple Views
We make the observation that features in the feature maps are viewpoint-dependent, and propose a method for transforming features with dynamic filters generated by a multi-layer perceptron from the relative poses between views.
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset.
Rainy screens: Collecting rainy datasets, indoors
Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions.
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
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.
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment.
Self-Supervised Localisation between Range Sensors and Overhead Imagery
Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable.
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning
In this work, we train a network to simultaneously perform segmentation and pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of unknown regions of scenes can be rejected.
Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos
We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving place recognition problem using complex radar data.
The Oxford Road Boundaries Dataset
In this paper we present the Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches.
Contrastive Learning for Unsupervised Radar Place Recognition
We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving the place recognition problem with complex radar data.
Depth-SIMS: Semi-Parametric Image and Depth Synthesis
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 maps.
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching.
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations
For this, we propose representing images - and by extension datasets - using Distributions of Neuron Activations (DNAs).
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation
In the case of localisation, important dataset differences impacting performance are modes of appearance change, including weather, lighting, and season.
Open-RadVLAD: Fast and Robust Radar Place Recognition
We achieve a median of 91. 52% in Recall@1, outstripping the 69. 55% for the only other open implementation, RaPlace, and at a fraction of its computational cost (relying on fewer integral transforms e. g. Radon, Fourier, and inverse Fourier).
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
Knowing when a trained segmentation model is encountering data that is different to its training data is important.
Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass.
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation.
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition
Two parallel lines of work on VPR have shown, on one side, that general-purpose off-the-shelf feature representations can provide robustness to domain shifts, and, on the other, that fused information from sequences of images improves performance.
