Preregistration classification of mobile LIDAR data using spatial correlations

dc.contributorAalto Universityen
dc.contributor.authorLehtola, Ville V.en_US
dc.contributor.authorLehtomaki, Mattien_US
dc.contributor.authorHyyti, Heikkien_US
dc.contributor.authorKaijaluoto, Ristoen_US
dc.contributor.authorKukko, Anteroen_US
dc.contributor.authorKaartinen, Harrien_US
dc.contributor.authorHyyppa, Juhaen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.organizationFinnish Geospatial Research Instituteen_US
dc.description.abstractWe explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data.en
dc.description.versionPeer revieweden
dc.identifier.citationLehtola, V V, Lehtomaki, M, Hyyti, H, Kaijaluoto, R, Kukko, A, Kaartinen, H & Hyyppa, J 2019, ' Preregistration classification of mobile LIDAR data using spatial correlations ', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, 8700597, pp. 6900-6915 .
dc.identifier.otherPURE UUID: e27b96f1-b4bf-42df-af03-9e07480169b9en_US
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dc.relation.ispartofseriesIEEE Transactions on Geoscience and Remote Sensingen
dc.relation.ispartofseriesVolume 57, issue 9en
dc.subject.keywordClassification algorithmsen_US
dc.subject.keywordLaser radaren_US
dc.subject.keywordMachine visionen_US
dc.subject.keywordRemote sensingen_US
dc.subject.keywordSimultaneous localization and mappingen_US
dc.titlePreregistration classification of mobile LIDAR data using spatial correlationsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi