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Updated: Oct 30, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

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Published on: December 1, 2016

Pose Estimation of Omnidirectional Camera with Improved EPnP Algorithm.

Xuanrui Gong¹, Yaowen Lv¹, Xiping Xu¹

¹College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

This study introduces a linear imaging model for omnidirectional cameras, improving pose estimation accuracy. The enhanced EPnP algorithm demonstrates robust performance, even with noisy data from 360° imaging.

Keywords:

Perspective-n-Point omnidirectional camera pose estimation

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Area of Science:

Computer Vision
Robotics
Optical Engineering

Background:

Omnidirectional cameras offer a wide field of view (360° horizontal imaging) but introduce nonlinear distortions due to mirror reflections.
Estimating the pose of omnidirectional cameras is challenging due to altered collinearity and scene distortions.

Purpose of the Study:

To develop a method for accurate omnidirectional camera pose estimation.
To address the nonlinear distortions inherent in 360° imaging systems.

Main Methods:

Derived a mapping from omnidirectional to traditional camera models.
Proposed a novel omnidirectional camera linear imaging model.
Improved the EPnP algorithm for pose calculation based on the linear model.

Main Results:

Validated the proposed linear imaging model through simulations and physical experiments.
The improved EPnP algorithm effectively calculates omnidirectional camera pose.
The method demonstrated good noise resistance in pose estimation.

Conclusions:

The proposed linear imaging model and enhanced EPnP algorithm provide a viable solution for accurate omnidirectional camera pose estimation.
This approach effectively mitigates the challenges posed by nonlinear distortions in 360° imaging.