TY - GEN
T1 - Design of 3D LiDAR Combined with Neural Network for Object Classification
AU - Abdurrohman Asyari, Basith
AU - Rivai, Muhammad
AU - Attamimi, Muhammad
AU - Purwanto, Djoko
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - LiDAR is one of the visual sensors which can measure a distance and form an environment description. This device is needed for many kinds of vehicle navigation especially for the autonomous system. Nowadays, the 3D LiDAR is still expensive in the market. This study has developed and constructed a 3D LiDAR consisting of a single point LiDAR as the main sensor and a Neural Network for classifying objects. Proportional-integral-derivative (PID) controller was involved to maintain the motor rotation in order to stabilize the scanning process. Arduino Mega microcontroller was used as the main processor to obtain the LiDAR data, to control the motor speed, and to communicate the data with computer. In this case, the 3D LiDAR was tested using five different objects. The experimental results show that the system can recognize all objects with a 100% success rate. This proposed system can be expected to support the road safety on an autonomous vehicle. In addition, the 3D LiDAR can be marketed in a low price.
AB - LiDAR is one of the visual sensors which can measure a distance and form an environment description. This device is needed for many kinds of vehicle navigation especially for the autonomous system. Nowadays, the 3D LiDAR is still expensive in the market. This study has developed and constructed a 3D LiDAR consisting of a single point LiDAR as the main sensor and a Neural Network for classifying objects. Proportional-integral-derivative (PID) controller was involved to maintain the motor rotation in order to stabilize the scanning process. Arduino Mega microcontroller was used as the main processor to obtain the LiDAR data, to control the motor speed, and to communicate the data with computer. In this case, the 3D LiDAR was tested using five different objects. The experimental results show that the system can recognize all objects with a 100% success rate. This proposed system can be expected to support the road safety on an autonomous vehicle. In addition, the 3D LiDAR can be marketed in a low price.
KW - LiDAR
KW - Neural Network
KW - object classification
KW - road safety
UR - http://www.scopus.com/inward/record.url?scp=85138698583&partnerID=8YFLogxK
U2 - 10.1109/ICISIT54091.2022.9872713
DO - 10.1109/ICISIT54091.2022.9872713
M3 - Conference contribution
AN - SCOPUS:85138698583
T3 - 2022 1st International Conference on Information System and Information Technology, ICISIT 2022
SP - 336
EP - 341
BT - 2022 1st International Conference on Information System and Information Technology, ICISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Information System and Information Technology, ICISIT 2022
Y2 - 27 July 2022 through 28 July 2022
ER -