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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Quantitative Autonomic Testing
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Enabling Autonomous Navigation for Affordable Scooters.

Kaikai Liu1, Rajathswaroop Mulky2

  • 1Computer Engineering, San Jose State University, San Jose, CA 95123, USA. kaikai.liu@sjsu.edu.

Sensors (Basel, Switzerland)
|June 8, 2018
PubMed
Summary

This study introduces an autonomous scooter to enhance mobility for individuals with challenges. It uses sensor fusion for safe, independent indoor navigation in unknown environments.

Keywords:
SLAMautonomous systemnavigationobject detectionscootersensor fusion

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Area of Science:

  • Robotics
  • Assistive Technology
  • Computer Vision

Background:

  • Existing assistive devices like electric scooters offer limited autonomy.
  • Independent navigation for people with mobility challenges in unfamiliar indoor settings remains difficult.

Purpose of the Study:

  • To design an autonomous scooter for safe and independent indoor navigation.
  • To enhance the safety and autonomy of mobility devices for individuals with physical challenges.

Main Methods:

  • Developed a semi-LiDAR system using gyro-based pose data for real-time laser motion compensation.
  • Employed dynamic sensor fusion of stereo vision, synthetic laser scanning, and LiDAR for comprehensive environmental mapping.
  • Implemented self-correction algorithms for data fusion errors to create a hybrid map.

Main Results:

  • Successfully created synthetic maps of simple indoor environments.
  • Achieved simultaneous fine-grained resolution and long-range coverage in mapping complex environments.
  • Developed a hybrid map enabling collision-free navigation for the autonomous scooter.

Conclusions:

  • The proposed autonomous scooter design significantly improves navigation safety and independence for users with mobility challenges.
  • Dynamic sensor fusion and error correction are key to effective autonomous indoor navigation in complex, unknown environments.