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Summary & Details

SPARSE POINT CLOUD INTERPOLATION METHOD USING SENSOR FUSION WITH VIRTUAL GRADIENT

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Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20219041

Paper/Info type: Other International Conferences

Pages: 1-3(Total 3 p)

Date of publication: Sep 2021

Publisher: JSAE

Language: English

Detailed Information

Author(E)	1) Shuncong Shen, 2) Mai Saito, 3) Toshio Ito
Affiliation(E)	1) Shibaura Institute of Technology, 2) Shibaura Institute of Technology, 3) Shibaura Institute of Technology
Abstract(E)	For automated driving, accurate detection and recognition of the surrounding environment are essential to ensure the safety driving. Light Detection And Ranging (LiDAR) has been used as an external recognition sensor, which plays a role in obstacle recognition, mapping, tracking, etc. LiDAR senses the environment by using laser light and provides excellent range information, however is also limited by distance. This paper proposes a method to obtain the depth information of feature points from objects motion by utilizing the conventional optical flow and using the virtual gradient method to increase the density, finally fuse them with the LiDAR point cloud data. Experimental results show that this method can solve the sparse data problem and the effectiveness for improving LiDAR point cloud density is discussed.

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