53 citations · 157 across the 20 of their papers we have counts for
23 papers
Viking Hill Dataset: A Lidar-Radar-Camera Dataset for Detection and Segmentation in Forest Scenes
Vladimír Kubelka, Oleksandr Kotlyar, Unal Artan +1
Autonomous robots operating under forest canopies need robust perception of trees and surrounding vegetation across varying seasonal conditions. Existing forestry datasets provide…
Legible and Intuitive Multi-modal Robot State and Intent Communication Validated in Online and Real-world Studies
Tim Schreiter, Jens V. Rüppel, Andrey Rudenko +2
Effective robot-to-human communication can increase transparency and trust, reduce uncertainty, and contribute to safer collaboration in shared workspaces. Designing and validating…
Large-scale visual SLAM for in-the-wild videos
Shuo Sun, Torsten Sattler, Malcolm Mielle +2
Accurate and robust 3D scene reconstruction from casual, in-the-wild videos can significantly simplify robot deployment to new environments. However, reliable camera pose estimatio…
THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Robot Interaction
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu +7
We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social navigation: e.g., modellin…
High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization
Shuo Sun, Malcolm Mielle, Achim J. Lilienthal +1
We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction. To this end, we first prop…
3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding
Shuo Sun, Malcolm Mielle, Achim J. Lilienthal +1
Neural implicit surface representations are currently receiving a lot of interest as a means to achieve high-fidelity surface reconstruction at a low memory cost, compared to tradi…
CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +4
Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularl…
Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
Tim Schreiter, Lucas Morillo-Mendez, Ravi T. Chadalavada +5
Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial…
A Data-Efficient Approach for Long-Term Human Motion Prediction Using Maps of Dynamics
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +2
Human motion prediction is essential for the safe and smooth operation of mobile service robots and intelligent vehicles around people. Commonly used neural network-based approache…
TBV Radar SLAM -- trust but verify loop candidates
Daniel Adolfsson, Mattias Karlsson, Vladimír Kubelka +2
Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present T…
Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2
This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across ve…
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu +9
Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co…
CorAl: Introspection for Robust Radar and Lidar Perception in Diverse Environments Using Differential Entropy
Daniel Adolfsson, Manuel Castellano-Quero, Martin Magnusson +2
Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and preprocessing, as well…
Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps
Matteo Luperto, Tomasz Piotr Kucner, Andrea Tassi +2
Identifying the environment's structure, i.e., to detect core components as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonom…
Oriented surface points for efficient and accurate radar odometry
Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2
This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth…
CorAl -- Are the point clouds Correctly Aligned?
Daniel Adolfsson, Martin Magnusson, Qianfang Liao +2
In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and wi…
BFAR-Bounded False Alarm Rate detector for improved radar odometry estimation
Anas Alhashimi, Daniel Adolfsson, Martin Magnusson +2
This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Conti…
CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry
Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2
This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keep…
Robust Frequency-Based Structure Extraction
Tomasz Piotr Kucner, Matteo Luperto, Stephanie Lowry +2
State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder…
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
Li Sun, Daniel Adolfsson, Martin Magnusson +3
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based loca…
A method to segment maps from different modalities using free space layout -- MAORIS : MAp Of RIpples Segmentation
Malcolm Mielle, Martin Magnusson, Achim J. Lilienthal
How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction…
2D Map Alignment With Region Decomposition
Saeed Gholami Shahbandi, Martin Magnusson
In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map bu…
SLAM auto-complete: completing a robot map using an emergency map
Malcolm Mielle, Martin Magnusson, Henrik Andreasson +1
In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots i…