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Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry

2021-03-20 16:29:01
Qadeer Khan, Patrick Wenzel, Daniel Cremers

Abstract

tract: Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually difficult and expensive to obtain. In this paper, we demonstrate how a model can be trained to control a vehicle's trajectory using camera poses estimated through visual odometry methods in an entirely self-supervised fashion. We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car. Experimental results on the CARLA simulator demonstrate that our proposed approach performs at par with the model trained with supervision.

Abstract (translated)

URL

https://arxiv.org/abs/2103.11204

PDF

https://arxiv.org/pdf/2103.11204


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