微惯性传感器加速的序列图像三维重建方法

赵春宇; 孙伟; 陈许蒙

doi:10.3788/OPE.20162413.0559

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微惯性传感器加速的序列图像三维重建方法

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- 微惯性传感器加速的序列图像三维重建方法
- Accelerated 3D reconstruction method from image sequence based on inertial measurement unit
- 光学精密工程 2016年24卷第10s期页码：559-566
- 作者机构：
  
  西安电子科技大学空间科学与技术学院,陕西西安,710118
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年项目《视觉原理指导下的动目标检测与跟踪新方法研究》（No.61201290）();国家自然科学基金面上资助项目《基于视觉仿生的无人机环境感知关键技术
- DOI：10.3788/OPE.20162413.0559
  中图分类号： TP751.1
- 收稿日期：2016-04-19，
  
  修回日期：2016-05-31，
  
  纸质出版日期：2016-11-14
- 稿件说明：
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赵春宇, 孙伟, 陈许蒙. 微惯性传感器加速的序列图像三维重建方法[J]. 光学精密工程, 2016,24(10s): 559-566

ZHAO Chun-yu, SUN Wei, CHEN Xu-meng. Accelerated 3D reconstruction method from image sequence based on inertial measurement unit[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 559-566
赵春宇, 孙伟, 陈许蒙. 微惯性传感器加速的序列图像三维重建方法[J]. 光学精密工程, 2016,24(10s): 559-566 DOI： 10.3788/OPE.20162413.0559.

ZHAO Chun-yu, SUN Wei, CHEN Xu-meng. Accelerated 3D reconstruction method from image sequence based on inertial measurement unit[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 559-566 DOI： 10.3788/OPE.20162413.0559.

摘要

针对运动恢复结构（SfM）算法求取姿态计算复杂度高的问题，提出了微惯性传感器（IMU）加速的序列图像三维重建方法。利用加速度计计算图像拍摄时刻，结合IMU传感器信息，解算地理坐标系下移动终端拍摄图像时的初始姿态矩阵与位置信息，通过光束平差法去除传感器噪声，使用全局一致性转换获取图像坐标系下的移动终端位姿信息。本方法利用IMU信息恢复序列图像对应的相机位姿，代替SfM算法中遍历匹配特征点求相机位姿的过程，简化了三维结构恢复的计算。实验结果表明：本方法将SfM算法三维结构恢复的速度提升了3倍，三角法重建模型与实际长度的误差低于4.9%，有效降低了SfM方法的计算复杂度，可用于大规模场景的快速重建。

Abstract

Against the high complexity of posture calculation in Structure from Motion (SfM) algorithm

this paper proposed a method of accelerated 3D reconstruction of sequence image based on inertial measurement unit (IMU). Firstly

the captured moments of sequence image were caculated by accelerometer. Then

the initial orientation matrix and position information of mobile terminal at the captured moments were resolved according to moments and IMU's output data. Finally

sensor noise was removed by bundle adjustment

combined with global consistency rotation

the camera's pose information corresponding to sequence image were generated. Since we obtain the camera's position and orientation by the IMU's information

and the process of computing camera pose by traversing matching feature points in SfM algorithm was replaced

and the time consumption of 3D structure recovery was reduced. Experimental results show that the speed of 3D structure recovery in SfM algorithm is improved by 3 times

and the error between the model calculated length and the actual length is within 4.9%. The proposed approach effectively solves the time-consume problem with the structure from motion algorithm

it can be used for the rapid reconstruction of large scale scene.

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references

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