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Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review.

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Indoor localization using robots is challenging due to signal obstruction. This review covers sensor modalities like IMUs, LiDAR, radar, and cameras for robot odometry, enhancing indoor navigation capabilities.

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Area of Science:

  • Robotics and Sensor Technology
  • Indoor Navigation Systems
  • Polymer Science in Sensors

Background:

  • Global Navigation Satellite Systems (GNSSs) offer limited accuracy for indoor localization due to signal obstruction.
  • Self-contained localization schemes are crucial for reliable indoor navigation of moving robots.
  • Modern robots utilize advanced sensors and algorithms for environmental perception and localization.

Purpose of the Study:

  • To provide a comprehensive review of sensor modalities for indoor odometry.
  • To analyze algorithms and fusion frameworks for robot pose estimation and odometry.
  • To outline the principles and applications of indoor odometry for robotic systems.

Main Methods:

  • Review of Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radar, and cameras for indoor odometry.
  • Exploration of polymer applications within these sensor technologies.
  • Analysis of algorithms and sensor fusion frameworks for pose estimation.

Main Results:

  • Detailed examination of various sensor modalities and their suitability for indoor odometry.
  • Discussion on the integration of different sensors and algorithms for enhanced localization accuracy.
  • Identification of polymer applications contributing to sensor performance in indoor environments.

Conclusions:

  • Indoor odometry is a viable alternative to GNSS for robotic localization.
  • Sensor fusion and advanced algorithms are key to achieving robust indoor pose estimation.
  • This review provides a foundational understanding and future outlook for indoor odometry research.