论文标题
自动驾驶的环境视图鱼眼相机感知：概述，调查和挑战
Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges
论文作者
论文摘要
环绕视图的鱼眼相机通常用于自动驾驶中的近场感测。车辆四个侧面的四个鱼眼摄像机足以覆盖车辆周围捕获整个近场区域的360°。一些主要用例是自动停车，交通拥堵辅助和城市驾驶。数据集有限，而在近场感知任务上的工作很少，因为汽车感知的重点是远场感知。与远场相反，环绕视感受构成了额外的挑战，这是由于10厘米的高精度对象检测要求和对象的部分可见性。由于鱼眼摄像机的径向变形很大，因此无法轻松地扩展到环绕式用例。因此，我们有动力为研究人员和从业人员提供自动化的Fisheye摄像头感知的独立参考。首先，我们提供了常用的鱼眼摄像头模型的统一和分类治疗。其次，我们讨论各种感知任务和现有文献。最后，我们讨论挑战和未来方向。
Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.
