Joint optimization of depth and ego-motion for intelligent autonomous vehicles

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dc.contributor.authorGao, Yongbin
dc.contributor.authorTian, Fangzheng
dc.contributor.authorLi, Jun
dc.contributor.authorFang, Zhijun
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorSong, Wei
dc.contributor.authorYan, Yier
dc.date.accessioned2022-04-05T10:38:49Z
dc.date.available2022-04-05T10:38:49Z
dc.date.issued2022-03-24
dc.description.abstractThe three-dimensional (3D) perception of autonomous vehicles is crucial for localization and analysis of the driving environment, while it involves massive computing resources for deep learning, which can't be provided by vehicle-mounted devices. This requires the use of seamless, reliable, and efficient massive connections provided by the 6G network for computing in the cloud. In this paper, we propose a novel deep learning framework with 6G enabled transport system for joint optimization of depth and ego-motion estimation, which is an important task in 3D perception for autonomous driving. A novel loss based on feature map and quadtree is proposed, which uses feature value loss with quadtree coding instead of photometric loss to merge the feature information at the texture-less region. Besides, we also propose a novel multi-level V-shaped residual network to estimate the depths of the image, which combines the advantages of V-shaped network and residual network, and solves the problem of poor feature extraction results that may be caused by the simple fusion of low-level and high-level features. Lastly, to alleviate the influence of image noise on pose estimation, we propose a number of parallel sub-networks that use RGB image and its feature map as the input of the network. Experimental results show that our method significantly improves the quality of the depth map and the localization accuracy and achieves the state-of-the-art performance.en_UK
dc.identifier.citationGao Y, Tian F, Li J, et al., (2023) Joint optimization of depth and ego-motion for intelligent autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems, Volume 24, Issue 7, July 2023, pp. 7390-7403en_UK
dc.identifier.eissn1558-0016
dc.identifier.issn1524-9050
dc.identifier.urihttps://doi.org/10.1109/TITS.2022.3159275
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17738
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectIntelligent autonomous vehiclesen_UK
dc.subject6Gen_UK
dc.subjectdepth estimationen_UK
dc.subjectego-motionen_UK
dc.subjectfeature quadtreeen_UK
dc.subjectparallel sub-networken_UK
dc.subjectV-shaped residual networken_UK
dc.titleJoint optimization of depth and ego-motion for intelligent autonomous vehiclesen_UK
dc.typeArticleen_UK

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