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Visual SLAM: What Are the Current Trends and What to Expect?
Ali Tourani1, Hriday Bavle1, Jose Luis Sanchez-Lopez1
1Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg.
Sensors (Basel, Switzerland)
|December 11, 2022
Summary
Visual Simultaneous Localization and Mapping (VSLAM) uses cameras for robot navigation, offering advantages over LiDAR. This survey reviews VSLAM advancements, challenges, and future trends in robotics.
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.
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