H
Henrik Andreasson
Researcher at Örebro University
Publications - 94
Citations - 2833
Henrik Andreasson is an academic researcher from Örebro University. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 30, co-authored 84 publications receiving 2374 citations. Previous affiliations of Henrik Andreasson include University of Tübingen.
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Journal ArticleDOI
Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations
TL;DR: This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT).
Proceedings ArticleDOI
3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera
TL;DR: A method to acquire a realistic, visually convincing 3D model of indoor office environments based on a mobile robot that is equipped with a laser range scanner and a panoramic camera is presented.
Proceedings ArticleDOI
Appearance-based loop detection from 3D laser data using the normal distributions transform
TL;DR: A new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation is proposed, and it is shown that the proposed method works well in different environments.
Proceedings ArticleDOI
That’s on my mind! robot to human intention communication through on-board projection on shared floor space
TL;DR: On-board intention projection on the shared floor space for communication from robot to human is proposed and shows that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot.
Proceedings ArticleDOI
Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter
TL;DR: A vision-based approach to self-localization that uses a novel scheme to integrate feature-based matching of panoramic images with Monte Carlo localization and a specially modified version of Lowe’s SIFT algorithm is used.