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Ulf Larsson

dblp:57/5600 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · unresolved

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% Motion planning and robot control · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › mobile robot control
trailer backing
0.011994
Navigating an Articulated Vehicle and Reversing with a Trailer · ICRA 1994

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

state estimation · 0.0laser beacon tracking · 0.0kalman filter · 0.0
YearPublicationVenuePosition
1994 Navigating an Articulated Vehicle and Reversing with a Trailer
abstract
This paper describes two related tests; to navigate an articulated lawn mower and to reverse a mobile robot with a trailer. In both cases the vehicles are to follow a prespecified trajectory. The navigation principal is based on measured directions to several identical beacons, consisting of strips of reflective tapes. The angular sensor is a rotating laser for the illumination of the beacons and a highly sensitive electro-optical receiver for detecting the directions to the beacons. A Kalman filter is used to combine the measurements from the odometers with the detected angles to the known position of the beacons. To measure the angle between the robot and the trailer the same laser was used. This was done by placing two reflective beacons on the trailer. The repeatability was within 2 centimetre at low speed. The navigation of these two different types of vehicles turns out to be, essentially, the same problem. The sensitivities are different. Emphasis is on robust state estimation.>
Ulf Larsson, Caj Zell, Kalevi Hyyppä, Åke Wernersson
ICRA1