Object classification integrating results of each scan line with low‐resolution LIDAR. Issue 8 (17th May 2019)

• Record Type: Journal Article
• Authors: Nagashima, Toru; Nagasaki, Takeshi; Matsubara, Hitoshi
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 14:Issue 8(2019)
• Page Start: 1203
• Page End: 1208
• Publication Date: 2019-05-17
• Subjects: pedestrian detection -- low‐resolution LIDAR -- deep neural network; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22919
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : 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.