Monocular depth estimation for vision-based vehicles based on a self-supervised learning method

Marco Tektonidis; David Monnin

doi:10.1117/12.2558478

23 April 2020 Monocular depth estimation for vision-based vehicles based on a self-supervised learning method

Marco Tektonidis, David Monnin

Proceedings Volume 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020; 114150C (2020) https://doi.org/10.1117/12.2558478
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Unsupervised depth estimation methods that allow training on monocular image data have emerged as a promising tool for monocular vision-based vehicles that are not equipped with a stereo camera or a LIDAR. Predicted depths from single images could be used, for example, to avoid obstacles in autonomous navigation, or to improve in-vehicle change detection. We employ a self-supervised depth estimation network to predict depth in monocular image sequences acquired by a military vehicle and a UGV. We trained the models on the KITTI dataset, and performed a fine-tuning on monocular image data for each vehicle. The results illustrate that the estimated depths are visually plausible for on-road as well as for off-road environments. We also provide an example application by using the predicted depths for computing stixels, a medium-level representation of traffic scenes for self-driving vehicles.

Citation Download Citation

Marco Tektonidis and David Monnin "Monocular depth estimation for vision-based vehicles based on a self-supervised learning method", Proc. SPIE 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, 114150C (23 April 2020); https://doi.org/10.1117/12.2558478

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