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

Autor:	Khan, Qadeer, Wenzel, Patrick, Cremers, Daniel
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.11204 Zobrazit plný text záznamu View this record from Arxiv