Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Martin Persson; Tom Duckett; Achim J. Lilienthal

doi:10.1016/j.robot.2008.03.002

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Martin Persson, Tom Duckett, Achim J. Lilienthal

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

10 Zitate (Scopus)

Abstract

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

Originalsprache	Englisch
Seiten (von - bis)	483-492
Seitenumfang	10
Fachzeitschrift	Robotics and Autonomous Systems
Jahrgang	56
Ausgabenummer	6
DOIs	https://doi.org/10.1016/j.robot.2008.03.002
Publikationsstatus	Veröffentlicht - 30 Juni 2008
Extern publiziert	Ja

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title = "Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping",

abstract = "This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.",

keywords = "Aerial image, Mobile robot, Semantic mapping, Semi-supervised learning",

author = "Martin Persson and Tom Duckett and Lilienthal, {Achim J.}",

note = "Funding Information: The authors gratefully acknowledge the anonymous reviewers for their valuable comments and Christoffer Valgren for the implementation of the modified flood fill algorithm. Martin Persson was supported by the Swedish Defence Material Administration. ",

year = "2008",

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doi = "10.1016/j.robot.2008.03.002",

language = "English",

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AU - Persson, Martin

AU - Duckett, Tom

AU - Lilienthal, Achim J.

N1 - Funding Information: The authors gratefully acknowledge the anonymous reviewers for their valuable comments and Christoffer Valgren for the implementation of the modified flood fill algorithm. Martin Persson was supported by the Swedish Defence Material Administration.

PY - 2008/6/30

Y1 - 2008/6/30

N2 - This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

AB - This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

KW - Aerial image

KW - Mobile robot

KW - Semantic mapping

KW - Semi-supervised learning

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SN - 0921-8890

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SP - 483

EP - 492

JO - Robotics and Autonomous Systems

JF - Robotics and Autonomous Systems

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Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Abstract

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