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Monocular SLAM with conditionally independent split mapping.

Steven A Holmes1, David W Murray

  • 1Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom. sah@robots.ox.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 20, 2013
PubMed
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This study introduces SCISM, a novel algorithm for real-time structure from motion. SCISM efficiently manages computational complexity by optimizing local and bulk maps simultaneously for robust 3D reconstruction.

Area of Science:

  • Computer Vision
  • Robotics
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Real-time structure from motion over large areas faces challenges from computational complexity and inconsistent calculations.
  • Existing methods struggle to balance continuous exploration with background optimization for large-scale mapping.

Purpose of the Study:

  • To develop an efficient algorithm, SCISM, for real-time structure from motion recovery.
  • To mitigate computational complexity and arithmetical inconsistency in large-scale mapping.
  • To enable continuous local map exploration alongside background global map optimization.

Main Methods:

  • Developed SCISM, an algorithm based on relative frame bundle adjustment.
  • Split 3D landmark and keyframe pose recovery into local and bulk map optimization.

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  • Implemented SCISM using the Parallel Tracking and Mapping (PTAM) method with relative frame representation.

    • Main Results:

    • Demonstrated SCISM's real-time operation in outdoor environments.
    • Showcased the algorithm's ability to maintain consistency between local and bulk maps.
    • Provided evidence of improved performance compared to conventional submapping approaches.

    Conclusions:

    • SCISM effectively addresses computational complexity and arithmetical inconsistency in real-time structure from motion.
    • The relative frame representation allows seamless integration of background optimization into real-time processes.
    • SCISM offers a robust solution for large-scale, real-time 3D mapping applications.