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Topometric imitation learning for route following under appearance change
Cai, Shaojun1; Wan, Yingjia2
Correspondent ;
Conference Name2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Source PublicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Conference Date2020, June 14, 2020 - June 19, 2020
Conference Place不详
Contribution Rank2

Traditional navigation models in autonomous driving rely heavily on metric maps, which severely limits their application in large scale environments. In this paper, we introduce a two-level navigation architecture that contains a topological-metric memory structure and a deep image-based controller. The hybrid memory extracts visual features at each location point with a deep convolutional neural network, and stores information about local driving commands at each location point based on metric information estimated from egomotion information. The topological-metric memory is seamlessly integrated with a conditional imitation learning controller through the navigational commands that drives the vehicle between different vertices without collision. We test the whole system in teach-and-repeat experiments in an urban driving simulator. Results show that after being trained in a separate environment, the system could quickly adapt to novel environments with a single teach trial and follow route successively under various illumination and weather conditions.

Indexed ByEI
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Document Type会议论文
Affiliation1.UISEE Technology Inc., 85 Hongan Road, Fangshan District, Beijing, China
2.Chinese Academy of Sciences, Key Laboratory of Behavioral Sciences, Institute of Psychology, 16 Lincui Road, Chaoyang District, Beijing, China
Recommended Citation
GB/T 7714
Cai, Shaojun,Wan, Yingjia. Topometric imitation learning for route following under appearance change[C],2020:4354-4362.
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