4 citations · 8 across the 9 of their papers we have counts for
9 papers
Uncertainty-Informed Active Perception for Open Vocabulary Object Goal Navigation
Utkarsh Bajpai, Julius Rückin, Cyrill Stachniss +1
Mobile robots exploring indoor environments increasingly rely on vision-language models to perceive high-level semantic cues in camera images, such as object categories. Such model…
Active Learning of Robot Vision Using Adaptive Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonom…
Towards Map-Agnostic Policies for Adaptive Informative Path Planning
Julius Rückin, David Morilla-Cabello, Cyrill Stachniss +2
Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is…
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
Apoorva Vashisth, Julius Rückin, Federico Magistri +2
Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially…
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots…
NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering
Liren Jin, Xieyuanli Chen, Julius Rückin +1
Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. In this paper, we address the problem of positioning an RGB camera to co…
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping
Julius Rückin, Federico Magistri, Cyrill Stachniss +1
Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of ima…
Informative Path Planning for Active Learning in Aerial Semantic Mapping
Julius Rückin, Liren Jin, Federico Magistri +2
Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of h…
Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels
Liren Jin, Julius Rückin, Stefan H. Kiss +2
Unmanned aerial vehicles are rapidly gaining popularity in a variety of environmental monitoring tasks. A key requirement for their autonomous operation is the ability to perform e…