TY - GEN
T1 - Self-calibrated dense 3D sensor using multiple cross line-lasers based on light sectioning method and visual odometry
AU - Nagamatsu, Genki
AU - Takamatsu, Jun
AU - Iwaguchi, Takafumi
AU - Thomas, Diego Gabriel Francis
AU - Kawasaki, Hiroshi
N1 - Funding Information:
This work was supported by JSPS/KAKENHI 20H00611, 18K19824, 18H04119 in Japan.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Among various 3D capturing systems, since the system with line lasers based on the light sectioning method is simple and accurate, it has widely attracted many developers and used for many purposes. In addition, there is no need to synchronize the camera and the laser and also the configuration of the camera and the lasers is flexible, and thus, the system can be used for extreme conditions, such as underwater. There are two open problems for the system. The first problem is a low density of the 3D shape obtained from a single image, i.e., just several curves. The second problem is the accuracy of line detection in the wild. In this paper, we propose a self-calibration method using visual odometry (VO) to bundle a large number of frames to increase the density to solve the first problem. We also propose a robust line detection algorithm using CNN to solve the second problem. Comparative experiments prove the effectiveness of our proposed method. In addition, the system was tested in the extreme condition for demonstration.
AB - Among various 3D capturing systems, since the system with line lasers based on the light sectioning method is simple and accurate, it has widely attracted many developers and used for many purposes. In addition, there is no need to synchronize the camera and the laser and also the configuration of the camera and the lasers is flexible, and thus, the system can be used for extreme conditions, such as underwater. There are two open problems for the system. The first problem is a low density of the 3D shape obtained from a single image, i.e., just several curves. The second problem is the accuracy of line detection in the wild. In this paper, we propose a self-calibration method using visual odometry (VO) to bundle a large number of frames to increase the density to solve the first problem. We also propose a robust line detection algorithm using CNN to solve the second problem. Comparative experiments prove the effectiveness of our proposed method. In addition, the system was tested in the extreme condition for demonstration.
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U2 - 10.1109/IROS51168.2021.9636505
DO - 10.1109/IROS51168.2021.9636505
M3 - Conference contribution
AN - SCOPUS:85124373231
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 94
EP - 100
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -