Exploiting prior knowledge in mobile visual location recognition

Schroth, Georg; Huitl, Robert; Abu-Alqumsan, Mohammad; Schweiger, Florian; Steinbach, Eckehard

doi:10.1109/icassp.2012.6288388

Cited by 7 publications

(2 citation statements)

References 12 publications

(19 reference statements)

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“…This allows the client to localize itself for a limited time without communicating with the server (10 seconds in this case). This not only eliminates the network latency, but also effectively integrates prior knowledge into the retrieval system (see [4,12] for details). The average precision values for three query sequences are shown in Fig.…”

Section: Query Sequence With Ground Truthmentioning

confidence: 99%

TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping

Huitl

Schroth

Hilsenbeck

et al. 2012

2012 19th IEEE International Conference on Image Processing

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Recent advances in the field of content-based image retrieval (CBIR) have made it possible to quickly search large image databases using photographs or video sequences as a query. With appropriately tagged images of places, this technique can be applied to the problem of visual location recognition. While this task has attracted large interest in the community, most existing approaches focus on outdoor environments only. This is mainly due to the fact that the generation of an indoor dataset is elaborate and complex. In order to allow researchers to advance their approaches towards the challenging field of CBIR-based indoor localization and to facilitate an objective comparison of different algorithms, we provide an extensive, high resolution indoor dataset. The free for use dataset includes realistic query sequences with ground truth as well as point cloud data, enabling a localization system to perform 6-DOF pose estimation.

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Section: Query Sequence With Ground Truthmentioning

confidence: 99%

TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping

Huitl

Schroth

Hilsenbeck

et al. 2012

2012 19th IEEE International Conference on Image Processing

Self Cite

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“…Recently, content based image retrieval approaches have been successfully applied to location recognition in textured outdoor environments [1,2,11,10]. Indoor environments, however, are more challenging, as only few distinctive features are available and perspective distortion is more pronounced in narrow corridors.…”

Section: Related Workmentioning

confidence: 99%

Virtual reference view generation for CBIR-based visual pose estimation

Huitl

Schroth

Hilsenbeck

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

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Determining the pose of a mobile device based on visual information is a promising approach to solve the indoor localization problem. We present an approach that transforms localized images along a mapping trajectory into virtual viewpoints that cover a set of densely sampled camera positions and orientations in a confined environment. The viewpoints are represented by their respective bag-of-features vectors and image retrieval techniques are applied to determine the most likely pose of query images at very low computational complexity. As virtual image locations and orientations are decoupled from actual image locations, the system is able to work with sparse reference imagery and copes well with perspective distortion. Experiments confirm that pose retrieval performance is significantly improved.

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