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Manuel Lussi

dblp:226/6334 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 50% 3D vision · 50%
Computer graphics and multimedia
1 paper
Computational fabrication · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.312018
Accurate and Adaptive in Situ Fabrication of an Undulated Wall Using an on-Board Visual Sensing System · ICRA 2018
Computer vision › 3D vision › pose estimation
visual pose estimation
0.312018
Accurate and Adaptive in Situ Fabrication of an Undulated Wall Using an on-Board Visual Sensing System · ICRA 2018
Computational fabrication › computer-aided manufacturing
robotic fabrication
0.312018
Accurate and Adaptive in Situ Fabrication of an Undulated Wall Using an on-Board Visual Sensing System · ICRA 2018

Methods — techniques the papers use, named apart from their topics

vision-based sensing · 0.7CAD model referencing · 0.7
YearPublicationVenuePosition
2018 Accurate and Adaptive in Situ Fabrication of an Undulated Wall Using an on-Board Visual Sensing System
abstract
In this paper we present a system for the in situ33In the context of building construction, “in situ” means that fabrication takes place at the structure's final location directly on the building site. fabrication of a full-scale, load-bearing, and doubly-curved steel reinforced concrete wall. Two complementary vision-based sensing systems provide the feedback necessary to build a 12 meter long steel wire mesh as part of a novel digital building process. The sensing systems provide estimates of the robot pose, referenced to the CAD model of the building site, as well as feedback on the accuracy of the built structure over the course of construction. This second piece of information is used to adapt the building plan to compensate for system inaccuracies and material deformations which occur during buildup. In this way, the structure was successfully built with 98% of the total geometry within 2 centimeters of the designed position. To the best of our knowledge, this is the largest structure which has been built by a mobile robot using solely vision-based sensing.
Manuel Lussi, Timothy Sandy, Kathrin Dörfler, Norman Hack, Fabio Gramazio, Matthias Kohler, Jonas Buchli
ICRA1