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Freespace estimation in an autonomous wheelchair using a stereoscopic camera system.

Thanh H Nguyen1, Kiet T Vo

  • 1BioMedical Engineering Department, Ho Chi Minh International University, Viet Nam. thnguyen@eng.uts.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

This study introduces a Bayesian Recursive (BR) algorithm for autonomous wheelchairs to estimate safe passage areas using stereoscopic cameras. This method enhances navigation safety for disabled individuals by accurately assessing free space.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous wheelchairs require accurate perception of navigable space for safe operation.
  • Stereoscopic camera systems offer a cost-effective alternative to laser scanners for 3D environment mapping.
  • Bayesian methods provide a robust framework for handling uncertainty in real-time estimations.

Purpose of the Study:

  • To develop and validate a Bayesian Recursive (BR) algorithm for estimating freespace dimensions (height and width) for autonomous wheelchairs.
  • To utilize a stereoscopic camera system for generating 3D point clouds and converting them into 2D distance maps for freespace analysis.
  • To compare the performance of the proposed algorithm with laser-based freespace estimation methods.

Main Methods:

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  • A stereoscopic camera system captures 3D point cloud data.
  • Geometric projection and computation convert 3D point clouds into 2D distance maps.
  • A Bayesian Recursive (BR) algorithm estimates freespace using uncertainty information and control data from the 2D map.
  • Comparison with laser-derived 2D maps validates the approach.

    • Main Results:

    • The BR algorithm effectively estimates freespace dimensions from stereoscopic camera data.
    • The 2D distance maps derived from stereoscopic vision show comparable utility to laser-based maps for freespace estimation.
    • The algorithm successfully integrates uncertainty and control data for robust freespace assessment.

    Conclusions:

    • The proposed Bayesian Recursive algorithm, using stereoscopic vision, provides an effective method for autonomous wheelchair freespace estimation.
    • This approach enhances navigation safety and decision-making for mobile robotic platforms in indoor environments.
    • The study demonstrates the viability of stereoscopic cameras for reliable freespace perception in assistive robotics.