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Found 20 papers, 4 papers with code
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
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.
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.
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).
SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates.
LROC-PANGU-GAN: Closing the Simulation Gap in Learning Crater Segmentation with Planetary Simulators
We train a CycleGAN model to synthesise LROC from Planet and Asteroid Natural Scene Generation Utility (PANGU) images.
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.
