Localization is still one of the most challenging tasks in autonomous driving on city roads. Further development and improvement of automatic functions of vehicles in urban conditions are not possible without overcoming the problem of significant degradation of GNSS signal quality. The proposed approach to localization can provide information about vehicle position on the road in different operational conditions. Desired stability and quality are achieved by using the combination of conventional computer vision, neural networks and Kalman filtering.
Robust Localization of a Self-Driving Vehicle in a Lane
2020-09-01
1641078 byte
Conference paper
Electronic Resource
English
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