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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Highly Robust Vehicle Lateral Localization Using Multilevel Robust Network.

Zhiyong Zheng, Xu Li, Jianxiao Zhu

    IEEE Transactions on Neural Networks and Learning Systems
    |October 8, 2021
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    This study introduces a Multilevel Robust Network (MLRN) to improve vehicle lateral localization accuracy, even with road occlusions. The framework uses deep neural networks (DNNs) to overcome challenges posed by blocked road views.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Vision-based vehicle lateral localization is crucial for autonomous driving.
    • Occlusion by objects significantly degrades localization performance.
    • Existing methods struggle with frequent road visibility issues.

    Purpose of the Study:

    • To propose a robust lateral localization framework addressing occlusion challenges.
    • To enhance the reliability and accuracy of vehicle positioning systems.
    • To introduce the Multilevel Robust Network (MLRN) for improved performance.

    Main Methods:

    • Developed a Multilevel Robust Network (MLRN) using three deep neural networks (DNNs).
    • Implemented an attention-guided network (AGNet) for object-level road detection.
    • Utilized a lateral-connection fully convolutional denoising autoencoder (LC-FCDAE) for feature-level learning.
    • Employed a long short-term memory (LSTM) network for decision-level temporal correlation.

    Main Results:

    • MLRN demonstrated strong robustness against varying degrees of road occlusion.
    • The framework effectively reduced the impact of occluding objects on localization.
    • Experimental results confirmed improved reliability and accuracy in vehicle lateral localization.

    Conclusions:

    • The proposed MLRN framework significantly enhances vision-based vehicle lateral localization under occlusion.
    • The multi-level approach (object, feature, decision) effectively tackles localization challenges.
    • MLRN offers a reliable solution for autonomous driving systems requiring precise positioning.