Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to benchmark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the proposed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.
翻译:过去十年来,用于评估奥氏测量精确度的最相关公共数据集之一是 KITTI 数据集。 在质量和丰富的传感器设置之外,其成功还归功于在线评价工具,使研究人员能够对算法进行基准和比较。结果仅通过测试子集进行评估,对地面真相没有任何了解,产生不偏颇的、不完全的、不过分的、免费的和因此相关的根据照相机、3D激光或两者相结合的机器人本地化验证。然而,作为任何传感器设置,它需要事先校准和校正立体图像,从而引入对默认校准参数的依赖。鉴于这样,如果发现一套更好的校准参数能够产生更高的odo度精确度,就会产生一个自然问题。在本文件中,我们提出了对KITTI 数据集多摄像设置进行一次性校准的新办法。该办法可以产生更好的校准参数,无论是在低校准再测误差感和更低视觉测量误差的意义上,我们甚至进行了实验,其中我们展示了三种不同的测量算方法,即SOFT2、 ORA2、SL5D 和VISO2的精确度定分校准方法,在提议的ODB2中大大改进了我们的拟议的校正方法。