A deep learning approach for motion segment estimation for pipe leak detection robot

The trajectory motion of a robot can be a valuable information to estimate the localization of an autonomous robotic system, especially in a very dynamic but structurally-known environments like water pipes where the sensor readings are not reliable. The main focus of this research is to estimate th...

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Bibliographic Details
Published in:Procedia computer science 2019, Vol.158, p.37-44
Main Authors: Uyanik, Cihan, Erdemir, Erdem, Kaplanoglu, Erkan, Sekmen, Ali
Format: Article
Language:English
Subjects:
Convolutional Neural Network
Deep Learning
Inertial Measurement Unit
Leak Detection Robot
Motion Trajectory
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Summary:The trajectory motion of a robot can be a valuable information to estimate the localization of an autonomous robotic system, especially in a very dynamic but structurally-known environments like water pipes where the sensor readings are not reliable. The main focus of this research is to estimate the location of meso-scale robots using a deep-learning-based motion trajectory segment detection system from recorded sensory measurements while the robot travels through a pipe system. The idea is based on the classification of the motion measurements, acquired by inertial measurement unit (IMU), by exploiting the deep learning approach. Proposed idea and utilized methodology are explained in the related sections and it is observed that convolutional neural network approach is quite powerful to overcome the unreliability of IMU data.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2019.09.025