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 omnidirectional 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

Achim J. Lilienthal

Beveridge

Canny

Felzenszwalb

Freund

Früh

Gonzales

Huber

Martin Persson

Mayer

Mueller

Persson

Surmann

Thrun

Tom Duckett

Tupin

English

Crossref

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

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

Persson, Martin

Duckett, Tom

Lilienthal, Achim J.

Publikationer från Örebro universitet

This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors

Academic Archive On-line (Örebro University, Sweden)

Lilienthal, Achim

University of Lincoln Institutional Repository

A 3D laser range ﬁnder for autonomous mobile robots, in:

A computational approach for edge detection,

A decision-theoretic generalization of on-line learning and an application to boosting,

An automated mathod for large-scale, ground-based city model acquisition,

Appearance-based obstacle detection with monocular color vision, in:

Automatic object extraction from aerial imagery – a survey focusing on buildings, Computer vision and image understanding 74 (2)

Detection of building outlines based on the fusion of SAR and optical features,

Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,

Eﬃcient graph-based image segmentation,

Experimental analysis of overhead data processing to support long range navigation, in:

How easy is matching 2D line models using local search?,

Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information, in:

Large-scale loop-closing with pictorial matching, in:

Map learning and high-speed navigation in RHINO, in:

Map-based priors for localization, in:

MATLAB and Octave functions for computer vision and image processing,

Motion stereo for mobile robots,

Probabilistic semantic mapping with a virtual sensor for building/nature detection, in:

Sag¨ u´ es, Robust line matching and estimate of homographies simultaneously, in: Pattern Recognition and Image Analysis:

Self-supervised monocular road detection in desert terrain, in:

The MathWorks, Matlab 7.0, including Image Processing Toolbox 5.0,

Using a priori data for prediction and object recognition in an autonomous mobile vehicle, in:

Virtual sensor for human concepts – building detection by an outdoor mobile robot,

Vision-based drivable surface detection in autonomous ground vehicles, in:

Vision-based motion planning for an autonomous motorcycle on ill-structured road, in:

Yet another survey on image segmentation: Region and boundary information integration, in:

Digitala Vetenskapliga Arkivet - Academic Archive On-line

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

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Publikationer från Örebro universitet

Academic Archive On-line (Örebro University, Sweden)

Academic Archive On-line (Örebro University, Sweden)

University of Lincoln Institutional Repository

Publikationer från Örebro universitet

Digitala Vetenskapliga Arkivet - Academic Archive On-line

Digitala Vetenskapliga Arkivet - Academic Archive On-line