Zobrazeno 1 - 10
of 4 802
pro vyhledávání: '"Lilienthal A"'
Autor:
Zhu, Yufei, Rudenko, Andrey, Palmieri, Luigi, Heuer, Lukas, Lilienthal, Achim J., Magnusson, Martin
Maps of dynamics are effective representations of motion patterns learned from prior observations, with recent research demonstrating their ability to enhance performance in various downstream tasks such as human-aware robot navigation, long-term hum
Externí odkaz:
http://arxiv.org/abs/2410.12237
Publikováno v:
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
The human gaze is an important cue to signal intention, attention, distraction, and the regions of interest in the immediate surroundings. Gaze tracking can transform how robots perceive, understand, and react to people, enabling new modes of robot c
Externí odkaz:
http://arxiv.org/abs/2406.06300
Social robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily li
Externí odkaz:
http://arxiv.org/abs/2404.13432
Autor:
Hilger, Maximilian, Kubelka, Vladimír, Adolfsson, Daniel, Andreasson, Henrik, Lilienthal, Achim J.
Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates the use of 4D imaging radars for SLAM and analyzes the challenges in rob
Externí odkaz:
http://arxiv.org/abs/2404.03940
Autor:
Zhu, Yufei, Fan, Han, Rudenko, Andrey, Magnusson, Martin, Schaffernicht, Erik, Lilienthal, Achim J.
Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, including motion planning, tracking, human-robot interaction and safety m
Externí odkaz:
http://arxiv.org/abs/2403.13640
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 propose a Gaussian densification strategy based on the rendering loss to m
Externí odkaz:
http://arxiv.org/abs/2403.12535
Autor:
Schreiter, Tim, de Almeida, Tiago Rodrigues, Zhu, Yufei, Maestro, Eduardo Gutierrez, Morillo-Mendez, Lucas, Rudenko, Andrey, Palmieri, Luigi, Kucner, Tomasz P., Magnusson, Martin, Lilienthal, Achim J.
We present a new large dataset of indoor human and robot navigation and interaction, called TH\"OR-MAGNI, that is designed to facilitate research on social navigation: e.g., modelling and predicting human motion, analyzing goal-oriented interactions
Externí odkaz:
http://arxiv.org/abs/2403.09285
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 traditional explicit representations.However, state-of-the-art methods stil
Externí odkaz:
http://arxiv.org/abs/2401.07164
In this article, we describe the Registry of Scientometric Data Sources (RSDS) and several scientometric data sources recorded in this open registry that could be of interest for scientometricians, institutional researchers, librarians, practitioners
Externí odkaz:
http://arxiv.org/abs/2311.06157
While recommender systems have become an integral component of the Web experience, their heavy reliance on user data raises privacy and security concerns. Substituting user data with synthetic data can address these concerns, but accurately replicati
Externí odkaz:
http://arxiv.org/abs/2311.03488