Projects per year
Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future.
|Number of pages||14|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - Feb 2016|
|MoE publication type||A1 Journal article-refereed|
- Laser radar
- machine vision
- remote sensing
COMBAT: Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing - Point Cloud Ecosystem
Gullmets, H., Nieminen, J., Ahlavuo, M., Vaaja, M. T., Laitala, A., Julin, A., Hyyppä, H., Maksimainen, M., Ståhle, P., Jaalama, K., Virtanen, J., Luhtala, L., Haggren, H., Lehtola, V., Rantanen, T., Ingman, M. & Kauhanen, H.
01/05/2015 → 31/01/2018
Project: Academy of Finland: Strategic research funding