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Object classification integrating results of each scan line with low‐resolution LIDAR
Author(s) -
Nagashima Toru,
Nagasaki Takeshi,
Matsubara Hitoshi
Publication year - 2019
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22919
Subject(s) - lidar , line (geometry) , computer science , artificial intelligence , object (grammar) , computer vision , resolution (logic) , low resolution , high resolution , artificial neural network , remote sensing , geography , mathematics , geometry
To recognize objects by using low‐resolution LIDAR for autonomous cars, we proposed a method to calculate the independent results for each scan line before integrating them. In the proposed method, objects can be recognized in one learned model even if the number of scan line irradiated to objects is different. This brings an advantage of saving time for preparing some models and learning data. We tried to classify pedestrians, bicycles, motorbikes, cars, and other objects for evaluating the performance, and obtained an accuracy of 99.00%. In addition, we compared the proposed method with a fully connected neural network method and 2DCNN method, and showed the proposed method is more robust against the missing of scan lines. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.