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Instant outdoor localization and SLAM initialization from 2.5D maps.

Clemens Arth, Christian Pirchheim, Jonathan Ventura

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    Summary
    This summary is machine-generated.

    This study introduces a novel method for precise geo-localization and global tracking of mobile devices in cities. It enables robust 3D Simultaneous Localization and Mapping (SLAM) using 2.5D maps and semantic image analysis.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Geographic Information Systems

    Background:

    • Accurate geo-localization and global tracking of mobile devices in urban environments are crucial for various applications.
    • Existing Simultaneous Localization and Mapping (SLAM) methods often struggle with large-scale, untextured environments and require complex initialization.

    Purpose of the Study:

    • To develop a robust and efficient method for large-scale geo-localization and global tracking of mobile devices in urban outdoor settings.
    • To enable instantaneous initialization and global registration of SLAM maps using readily available 2.5D maps.

    Main Methods:

    • Localizing the first keyframe by matching image features to untextured 2.5D maps.
    • Estimating absolute camera orientation from straight line segments and translation via semantic segmentation and city map alignment.
    • Initializing and extending a 3D SLAM map in a global coordinate system using a model-supported SLAM approach.

    Main Results:

    • Demonstrated robustness and accuracy of the localization method on a challenging dataset.
    • Achieved unconstrained global SLAM mapping and tracking of arbitrary camera motion in multiple sequences.
    • Successfully integrated semantic information and 2.5D maps for precise localization and mapping.

    Conclusions:

    • The proposed method offers a significant advancement in large-scale geo-localization and global tracking for mobile devices.
    • It provides a reliable foundation for real-world applications requiring accurate 3D mapping and localization in urban environments.
    • The model-supported SLAM approach enhances the efficiency and accuracy of mapping in challenging conditions.