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Area of Science:

  • Robotics and Computer Vision

Background:

  • Visual Simultaneous Localization and Mapping (VSLAM) systems leverage cameras for pose estimation and map reconstruction.
  • VSLAM is favored over LiDAR-based methods due to lower cost, lighter weight, and richer environmental data.

Purpose of the Study:

  • To provide a comprehensive survey of recent advancements in VSLAM.
  • To discuss current challenges and future trends in the VSLAM field.

Main Methods:

  • Literature review of fifty impactful VSLAM articles.
  • Classification of VSLAM methods based on novelty, objectives, algorithms, and semantic level.

Main Results:

  • Identification of diverse VSLAM approaches using various camera types and datasets.
  • Analysis of VSLAM performance in indoor and outdoor conditions.

Conclusions:

  • VSLAM is a rapidly evolving research area with significant potential in robotics.
  • Understanding current trends and challenges is crucial for future VSLAM research and development.