68 citations · 108 across the 22 of their papers we have counts for
22 papers
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors
Matteo Sodano, Federico Magistri, Elias Marks +6
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can he…
Open-World Panoptic Segmentation
Matteo Sodano, Federico Magistri, Jens Behley +1
Robots need to be able to understand their surroundings in order to operate safely and robustly, and to interact with the surrounding environment. Robots deployed in unconstrained…
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds
Daniel Fusaro, Federico Magistri, Jens Behley +2
Accurate and consistent fruit monitoring over time is a key step toward automated agricultural production systems. However, this task is inherently difficult due to variations in f…
AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation
Arun N. Sivakumar, Federico Magistri, Mateus V. Gasparino +3
Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous…
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…
High-throughput 3D shape completion of potato tubers on a harvester
Pieter M. Blok, Federico Magistri, Cyrill Stachniss +3
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimat…
A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics
Federico Magistri, Thomas Läbe, Elias Marks +9
As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agr…
Open-World Semantic Segmentation Including Class Similarity
Matteo Sodano, Federico Magistri, Lucas Nunes +2
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand thei…
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…
Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino +2
Crops for food, feed, fiber, and fuel are key natural resources for our society. Monitoring plants and measuring their traits is an important task in agriculture often referred to…
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Elias Marks, Jonas Bömer, Federico Magistri +3
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment. Advanceme…
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
Jan Weyler, Federico Magistri, Elias Marks +6
The production of food, feed, fiber, and fuel is a key task of agriculture, which has to cope with many challenges in the upcoming decades, e.g., a higher demand, climate change, l…
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino +4
Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central componen…
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Yue Pan, Federico Magistri, Thomas Läbe +5
Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications i…
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…
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
Gianmarco Roggiolani, Matteo Sodano, Tiziano Guadagnino +3
Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accur…
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
Matteo Sodano, Federico Magistri, Tiziano Guadagnino +2
Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation…
Monitoring and mapping of crop fields with UAV swarms based on information gain
Carlos Carbone, Dario Albani, Federico Magistri +5
Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileg…
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 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…