Preregistration classification of mobile LIDAR data using spatial correlations
| Autor: | Matti Lehtomäki, Ville V. Lehtola, Antero Kukko, Heikki Hyyti, Juha Hyyppä, Risto Kaijaluoto, Harri Kaartinen |
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| Přispěvatelé: | Department of Earth Observation Science, UT-I-ITC-ACQUAL, Faculty of Geo-Information Science and Earth Observation, Finnish Geospatial Research Institute, Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Machine vision 0211 other engineering and technologies Point cloud 02 engineering and technology Simultaneous localization and mapping computer.software_genre ITC-HYBRID remote sensing Electrical and Electronic Engineering Radiometric calibration 021101 geological & geomatics engineering Classification algorithms laser radar Ranging machine vision Remote sensing Tree (data structure) Statistical classification Laser radar Lidar ITC-ISI-JOURNAL-ARTICLE General Earth and Planetary Sciences Noise (video) Data mining computer simultaneous localization and mapping |
| Zdroj: | IEEE transactions on geoscience and remote sensing, 57(9):8700597, 6900-6915. IEEE |
| ISSN: | 0196-2892 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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