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EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties.
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images
Implicit supervision trains the model by enforcing spatial consistency of the scene over time based on FV semantic sequences, while explicit supervision exploits BEV pseudolabels generated from FV semantic annotations and self-supervised depth estimates.
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning
Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously.
Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization
Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs).
Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots
Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots.
Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images
Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites.
