Self-Supervised Stereo Depth Estimation: Depth estimation in multiple environments through an adaptive CNN and IR light

Examensarbete för masterexamen

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Bibliographical item details
Type: Examensarbete för masterexamen
Title: Self-Supervised Stereo Depth Estimation: Depth estimation in multiple environments through an adaptive CNN and IR light
Authors: Nordh, Jonatan
Vikén, Marcus
Abstract: We have developed a complete depth sensor unit with a self-supervised neural network and stereo camera. The sensor is both adaptive during usage and can work in dark and low light environments with aid from IR spotlights. Disparity estimation via stereo cameras has shown great performance in combination with neural networks during recent years. The reason is because deep learning reduces the computational effort considerably compared to previous methods. However, the existing deep learning methods do not evaluate the depth measurements but rather the disparity estimation accuracy on available benchmark datasets. In difference to earlier work, this system has been evaluated with respect to depth measurement accuracy and suitable evaluation metrics have been developed. If the stereo camera is to be used as a reliable depth sensor the depth estimation quality needs to be ensured. From the thesis contributions a high-functional depth sensor unit can be developed with potential to surpass other sensors with respect to the amount of data obtained per second.
Keywords: Artificial Neural Network (ANN);Convolutional Neural Network (CNN);deep learning;self-supervised;machine learning;stereo vision;disparity estimation;nightvision;Oriented FAST and Rotated BRIEF (ORB)
Issue Date: 2021
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Series/Report no.: 2021:27
Collection:Examensarbeten för masterexamen // Master Theses

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