513 citations · 2.8k across the 104 of their papers we have counts for
22 papers · 1 filter
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…
Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories
Louis Wiesmann, Elias Marks, Saurabh Gupta +3
Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from…
Epipolar Attention Field Transformers for Bird's Eye View Semantic Segmentation
Christian Witte, Jens Behley, Cyrill Stachniss +1
Spatial understanding of the semantics of the surroundings is a key capability needed by autonomous cars to enable safe driving decisions. Recently, purely vision-based solutions h…
ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
Liren Jin, Xingguang Zhong, Yue Pan +3
Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene…
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…
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…
Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces
Tiziano Guadagnino, Benedikt Mersch, Ignacio Vizzo +5
LiDAR odometry is essential for many robotics applications, including 3D mapping, navigation, and simultaneous localization and mapping. LiDAR odometry systems are usually based on…
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors
Hyungtae Lim, Seoyeon Jang, Benedikt Mersch +3
Moving object segmentation (MOS) using a 3D light detection and ranging (LiDAR) sensor is crucial for scene understanding and identification of moving objects. Despite the availabi…
Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation
Balázs Opra, Betty Le Dem, Jeffrey M. Walls +2
Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This pap…
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…
3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation
Xingguang Zhong, Yue Pan, Cyrill Stachniss +1
Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate m…
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch +2
Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D…
STAIR: Semantic-Targeted Active Implicit Reconstruction
Liren Jin, Haofei Kuang, Yue Pan +2
Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is…
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…
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
Sicong Pan, Liren Jin, Xuying Huang +3
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations…
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…
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
Yue Pan, Xingguang Zhong, Louis Wiesmann +3
Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, cal…