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A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization

Author

Listed:
  • Minxiang Liu
  • Yuhao Wang
  • Henry Leung
  • Jiangnan Yu

Abstract

We present a novel feature-level data fusion method for autonomous localization in an inactive multiple reference unknown indoor environment. Since monocular sensors cannot provide the depth information directly, the proposed method incorporates the edge information of images from a camera with homologous depth information received from an infrared sensor. Real-time experimental results demonstrate that the accuracies of position and orientation are greatly improved by using the proposed fusion method in an unknown complex indoor environment. Compared to monocular localization, the proposed method is found to have up to 70 percent improvement in accuracy.

Suggested Citation

  • Minxiang Liu & Yuhao Wang & Henry Leung & Jiangnan Yu, 2013. "A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, July.
    • Handle: RePEc:hin:jnlmpe:382619
    DOI: 10.1155/2013/382619
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