Self-Supervised Stereo Depth Estimation: Depth estimation in multiple environments through an adaptive CNN and IR light
Typ
Examensarbete för masterexamen
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2021
Författare
Nordh, Jonatan
Vikén, Marcus
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
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)