Indoor Location Retrieval using Shape Matching of KinectFusion Scans to Large-Scale Indoor Point Clouds

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dc.contributor.authorAl-Nuaimi, Anasen_US
dc.contributor.authorPiccolrovazzi, Martinen_US
dc.contributor.authorGedikli, Suaten_US
dc.contributor.authorSteinbach, Eckeharden_US
dc.contributor.authorSchroth, Georgen_US
dc.contributor.editorI. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkampen_US
dc.date.accessioned2015-04-27T11:03:34Z
dc.date.available2015-04-27T11:03:34Z
dc.date.issued2015en_US
dc.description.abstractIn this paper we show that indoor location retrieval can be posed as a part-in-whole matching problem of Kinect- Fusion (KinFu) query scans in large-scale target indoor point clouds. We tackle the problem with a local shape feature-based 3D Object Retrieval (3DOR) system. We specifically show that the KinFu queries suffer from artifacts stemming from the non-linear depth distortion and noise characteristics of Kinect-like sensors that are accentuated by the relative largeness of the queries. We furthermore show that proper calibration of the Kinect sensor using the CLAMS technique (Calibrating, Localizing, and Mapping, Simultaneously) proposed by Teichman et al. effectively reduces the artifacts in the generated KinFu scan and leads to a substantial retrieval performance boost. Throughout the paper we use queries and target point clouds obtained at the world's largest technical museum. The target point clouds cover floor spaces of up to 3500m2. We achieve an average localization accuracy of 6cm although the KinFu query scans make up only a tiny fraction of the target point clouds.en_US
dc.description.sectionheaders3D Partial Shape Matching and Retrievalen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.identifier.doi10.2312/3dor.20151052en_US
dc.identifier.pages31-38en_US
dc.identifier.urihttps://doi.org/10.2312/3dor.20151052en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectLine and curve generationen_US
dc.titleIndoor Location Retrieval using Shape Matching of KinectFusion Scans to Large-Scale Indoor Point Cloudsen_US
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