6 citations · 12 across the 11 of their papers we have counts for
11 papers
UGV-Conditioned Multi-UAV Informative Planning on a Shared Exposure Belief
Lars Oerlemans, Moji Shi, Marija Popovic
Safe ground navigation in large, threat-augmented environments requires aerial support that actively reduces the risks that a ground vehicle faces along its route. Existing aerial…
MPPI-based Informative Trajectory Planning for Search and Capture of Drifting Targets with ASVs
Sanjeev Ramkumar Sudha, Marija Popović, Erlend M. Coates
Autonomous surface vehicles offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuous…
Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning
Jonas Westheider, Julius Rückin, Marija Popović
Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers i…
Graph-based View Motion Planning for Fruit Detection
Tobias Zaenker, Julius Rückin, Rohit Menon +2
Crop monitoring is crucial for maximizing agricultural productivity and efficiency. However, monitoring large and complex structures such as sweet pepper plants presents significan…
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…
3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation
Yifu Tao, Marija Popović, Yiduo Wang +3
Safe motion planning in robotics requires planning into space which has been verified to be free of obstacles. However, obtaining such environment representations using lidars is c…
Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation
Felix Stache, Jonas Westheider, Federico Magistri +2
Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs)…
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…
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
Felix Stache, Jonas Westheider, Federico Magistri +2
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain m…