TY - GEN
T1 - Neural network approach for vision-based track navigation using low-powered computers on MAVs
AU - Brahmbhatt, Khushal
AU - Pai, Akshatha Rakesh
AU - Singh, Sanjay
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/30
Y1 - 2017/11/30
N2 - A quadrotor Micro Aerial Vehicle (MAV) is designed to navigate a track using neural network approach to identify the direction of the path from a stream of monocular images received from a downward-facing camera mounted on the vehicle. Current autonomous MAVs mainly employ computer vision techniques based on image processing and feature tracking for vision-based navigation tasks. It requires expensive onboard computation and can create latency in the real-time system when working with low-powered computers. By using a supervised image classifier, we shift the costly computational task of training a neural network to classify the direction of the track to an off-board computer. We make use of the learned weights obtained after training to perform simple mathematical operations to predict the class of the image on the onboard computer. We compare the computer vision based tracking approach with the proposed approach to navigate a track using a quadrotor and show that the processing rates of the latter is faster. This allows low-cost, low-powered computers such as the Raspberry Pi to be used efficiently as onboard companion computers for flying vision-based autonomous missions with MAVs.
AB - A quadrotor Micro Aerial Vehicle (MAV) is designed to navigate a track using neural network approach to identify the direction of the path from a stream of monocular images received from a downward-facing camera mounted on the vehicle. Current autonomous MAVs mainly employ computer vision techniques based on image processing and feature tracking for vision-based navigation tasks. It requires expensive onboard computation and can create latency in the real-time system when working with low-powered computers. By using a supervised image classifier, we shift the costly computational task of training a neural network to classify the direction of the track to an off-board computer. We make use of the learned weights obtained after training to perform simple mathematical operations to predict the class of the image on the onboard computer. We compare the computer vision based tracking approach with the proposed approach to navigate a track using a quadrotor and show that the processing rates of the latter is faster. This allows low-cost, low-powered computers such as the Raspberry Pi to be used efficiently as onboard companion computers for flying vision-based autonomous missions with MAVs.
UR - http://www.scopus.com/inward/record.url?scp=85042915654&partnerID=8YFLogxK
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U2 - 10.1109/ICACCI.2017.8125902
DO - 10.1109/ICACCI.2017.8125902
M3 - Conference contribution
AN - SCOPUS:85042915654
VL - 2017-January
T3 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
SP - 578
EP - 583
BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Y2 - 13 September 2017 through 16 September 2017
ER -