Search Results for author:
Found 15 papers, 2 papers with code
Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse
This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse.
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.
Unsupervised Change Detection of Extreme Events Using ML On-Board
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.
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.
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
Robotics
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world.
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.
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.
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.
Online Fall Detection using Recurrent Neural Networks
This information can be used to trigger the necessary assistance in case of injury.
