WU Hua, WU Yanxiong, LIU Changan, YANG Guotian, LI Zhichen. Undelayed Initialization Using Dual Channel Vision for Ego-Motion in Power Line Inspection[J]. Chinese Journal of Electronics, 2016, 25(1): 33-39. doi: 10.1049/cje.2016.01.006
Citation: WU Hua, WU Yanxiong, LIU Changan, YANG Guotian, LI Zhichen. Undelayed Initialization Using Dual Channel Vision for Ego-Motion in Power Line Inspection[J]. Chinese Journal of Electronics, 2016, 25(1): 33-39. doi: 10.1049/cje.2016.01.006

Undelayed Initialization Using Dual Channel Vision for Ego-Motion in Power Line Inspection

doi: 10.1049/cje.2016.01.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61105083), Program for New Century Excellent Talents in University (No.NCET-11-0634), the Fundamental Research Funds for the Central Universities (No.12ZX16), and the Program of the Co-construction with Beijing Municipal of China (No.GJ2013005).
  • Received Date: 2015-02-27
  • Rev Recd Date: 2015-05-05
  • Publish Date: 2016-01-10
  • This paper presents a novel approach to the initialization of an ego-motion estimation technique for autonomous power line inspection. Dual channel vision, consisting of an infrared and optical camera, is typically adopted during inspection. The infrared camera is far more proficient at reliably detecting heated regions of the power tower which can be regarded as a prior relationship between the tower and cameras. Using the infrared camera, which is equipped parallel to the optical camera, an incomplete correspondence between the optical image and a 3D CAD model is established. Depending on the degree of correspondence, the initial pose of the CAD model in the optical image is estimated through two stages of coarse-to-fine estimation. The primary contributions of this paper include: 1) using dual vision for partial initialization; 2) incorporating two-stage algorithms to estimate an accurate pose quickly; 3) implementing an algorithm which functions correctly regardless of the motion blur or background texture. Experimental results consistently show that the initial pose can be estimated efficiently and robustly.
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