论文标题
校正自动驾驶车辆中立体声摄像机的脱校
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles
论文作者
论文摘要
我们解决了移动立体声摄像机设置中的光学取消校准问题,尤其是在自动驾驶汽车的情况下。在现实世界中,光学系统受到预期和意外的机械应力(振动,粗处理,碰撞)的各种来源。机械应力改变了构成立体声对的相机之间的几何形状,因此,预算的两极几何形状不再有效。我们的方法基于对摄像机几何参数的优化,并直接插入立体声匹配算法的输出中。因此,它能够通过最小使用其他计算资源来恢复从变色的立体声系统获得的图像对校准参数。成功恢复的深度像素的数量被用作目标函数,我们旨在最大化。我们的仿真确认该方法可以与立体声估计并行不断运行,从而有助于保持系统的实时校准。结果证实该方法能够重新校准除基线距离以外的所有参数,从而缩放了绝对深度读数。但是,可以使用任何类型的绝对范围查找方法(例如单光束飞行时间传感器)来唯一确定缩放因子。
We address the problem of optical decalibration in mobile stereo camera setups, especially in context of autonomous vehicles. In real world conditions, an optical system is subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions). Mechanical stress changes the geometry between the cameras that make up the stereo pair, and as a consequence, the pre-calculated epipolar geometry is no longer valid. Our method is based on optimization of camera geometry parameters and plugs directly into the output of the stereo matching algorithm. Therefore, it is able to recover calibration parameters on image pairs obtained from a decalibrated stereo system with minimal use of additional computing resources. The number of successfully recovered depth pixels is used as an objective function, which we aim to maximize. Our simulation confirms that the method can run constantly in parallel to stereo estimation and thus help keep the system calibrated in real time. Results confirm that the method is able to recalibrate all the parameters except for the baseline distance, which scales the absolute depth readings. However, that scaling factor could be uniquely determined using any kind of absolute range finding methods (e.g. a single beam time-of-flight sensor).