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On pose recovery for generalized visual sensors.

Chu-Song Chen1, Wen-Yan Chang

  • 1Institute of Information Science, Academia Sinica, Taipe, Taiwan.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 27, 2008
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Summary

This study introduces a new method for estimating the pose of generalized imaging devices, even those that do not follow traditional perspective rules. The nonperspective n-point (NPnP) algorithm accurately determines device poses for robot navigation and machine vision applications.

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Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Modeling

Background:

  • Advances in robot and machine vision necessitate new imaging devices.
  • Traditional imaging devices follow perspective rules, limiting their application in some scenarios.
  • Generalized imaging devices may not adhere to perspective rules, posing challenges for pose estimation.

Purpose of the Study:

  • To propose a systematic method for pose estimation of generalized imaging devices.
  • To address the nonperspective n-point (NPnP) problem for devices not following perspective rules.
  • To develop an accurate pose estimation technique applicable to both nonperspective and perspective imaging devices.

Main Methods:

  • Formulation of the nonperspective n-point (NPnP) problem.
  • Comprehensive investigation of the n=3 case for exact solutions.
  • Development of an iterative procedure for approximate solutions (n>3) using least-squared-error minimization, initial pose estimation, and orthogonal iteration.

Main Results:

  • An exact solution for the NPnP problem is derived for n=3.
  • Accurate approximate solutions are obtained for n>3 using the proposed iterative method.
  • Experimental results validate the accuracy and effectiveness of the developed approach.

Conclusions:

  • The proposed method provides a robust solution for pose estimation of generalized imaging devices.
  • The NPnP algorithm is effective for both nonperspective and conventional perspective imaging systems.
  • This work advances robot navigation and image-based rendering by enabling accurate pose estimation for a wider range of imaging devices.