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Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

机译:使用校准图案的带有导航系统的地面车辆上线扫描相机的外部参数校准

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摘要

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera’s 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera’s pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m/1.05° and 0.18 m/2.39°. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.
机译:仅捕获单个像素行的线扫描相机已越来越多地用于基于地面的移动或机器人平台中。在直接将摄影机数据地理参考到世界坐标上的优势的应用中,需要精确估算摄影机的6D姿态。本文重点介绍一种常见的情况,即移动平台配备了刚性安装的线扫描相机(其姿势未知)和导航系统(可提供车身姿势估计)。我们提出了一种新颖的方法来估算相机相对于导航系统的姿态。该方法涉及对具有明显可识别点的校准图案进行成像和手动标记,从摄像头和导航系统数据中对这些点进行三角测量,并对它们进行重新投影以计算可能性,该可能性被最大化以估计6D摄像机的姿态。另外,使用马尔可夫链蒙特卡洛(MCMC)算法来估计偏移量的不确定性。在两个不同的平台上进行测试,该方法能够将姿态估计在0.06 m / 1.05°和0.18 m / 2.39°范围内。我们还提出了几种以人类可读的方式显示和解释6D结果的方法。

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