A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations
Tekijät: Lehtola VV, Lehtomäki M, Hyyti H, Kaijaluoto R, Kukko A, Kaartinen H, Hyyppä J
Kustantaja: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Julkaisuvuosi: 2019
Lehti: IEEE Transactions on Geoscience and Remote Sensing
Tietokannassa oleva lehden nimi: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Lehden akronyymi: IEEE T GEOSCI REMOTE
Vuosikerta: 57
Numero: 9
Aloitussivu: 6900
Lopetussivu: 6915
Sivujen määrä: 16
ISSN: 0196-2892
eISSN: 1558-0644
DOI: https://doi.org/10.1109/TGRS.2019.2909351
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/42534570
We 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
