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Found 8 papers, 4 papers with code
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment.
STAIR: Semantic-Targeted Active Implicit Reconstruction
The key components of our framework are a semantic implicit neural representation and a compatible planning utility function based on semantic rendering and uncertainty estimation, enabling adaptive view planning to target objects of interest.
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments.
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
We propose a planning method for semi-supervised active learning of semantic segmentation that substantially reduces human labelling requirements compared to fully supervised approaches.
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping
Our framework combines the mapped acquisition function information into the UAV's planning objectives.
Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation
Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems.
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing
Aerial robots are increasingly being utilized for environmental monitoring and exploration.
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs).
