Search Results for author:
Found 10 papers, 1 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
