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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Advances in Visual Simultaneous Localisation and Mapping Techniques for Autonomous Vehicles: A Review.

Jibril Abdullahi Bala1, Steve Adetunji Adeshina2, Abiodun Musa Aibinu1

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Summary
This summary is machine-generated.

This review explores Visual Simultaneous Localisation and Mapping (V-SLAM) techniques for autonomous vehicles (AVs). It details V-SLAM

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Area of Science:

  • Robotics and Computer Vision

Background:

  • Autonomous vehicle (AV) development relies heavily on advanced Information and Communication Technology (ICT) for enhanced vehicular safety.
  • Perception and localization are critical functions for AVs, necessitating efficient environmental understanding and 3D mapping capabilities.

Purpose of the Study:

  • To present a comprehensive review of Visual Simultaneous Localisation and Mapping (V-SLAM) techniques applied to AV perception and localization.
  • To highlight the strengths, limitations, and challenges of V-SLAM deployment in autonomous systems.

Main Methods:

  • Overview of Simultaneous Localisation and Mapping (SLAM) principles.
  • In-depth review of various V-SLAM schemes and their performance metrics.
  • Analysis of current challenges and future research trajectories in V-SLAM for AVs.

Main Results:

  • V-SLAM enables AVs to perceive surroundings, map environments, and determine their precise location.
  • Identified various V-SLAM techniques with distinct advantages and disadvantages for AV applications.
  • Outlined key challenges hindering widespread V-SLAM adoption and potential solutions.

Conclusions:

  • V-SLAM is a pivotal technology for advancing AV perception and localization.
  • Further research is needed to overcome V-SLAM deployment challenges and enhance its robustness.
  • Continued development in V-SLAM will significantly contribute to the future of autonomous driving.