  Surround-view system (SVS) is widely used in the Advanced Driver Assistance
System (ADAS). SVS uses four fisheye lenses to monitor real-time scenes around
the vehicle. However, accurate intrinsic and extrinsic parameter estimation is
required for the proper functioning of the system. At present, the intrinsic
calibration can be pipeline by utilizing checkerboard algorithm, while
extrinsic calibration is still immature. Therefore, we proposed a specific
calibration pipeline to estimate extrinsic parameters robustly. This scheme
takes a driving sequence of four cameras as input. It firstly utilizes lane
line to roughly estimate each camera pose. Considering the environmental
condition differences in each camera, we separately select strategies from two
methods to accurately estimate the extrinsic parameters. To achieve accurate
estimates for both front and rear camera, we proposed a method that mutually
iterating line detection and pose estimation. As for bilateral camera, we
iteratively adjust the camera pose and position by minimizing texture and edge
error between ground projections of adjacent cameras. After estimating the
extrinsic parameters, the surround-view image can be synthesized by
homography-based transformation. The proposed pipeline can robustly estimate
the four SVS camera extrinsic parameters in real driving environments. In
addition, to evaluate the proposed scheme, we build a surround-view fisheye
dataset, which contains 40 videos with 32,000 frames, acquired from different
real traffic scenarios. All the frames in each video are manually labeled with
lane annotation, with its GT extrinsic parameters. Moreover, this surround-view
dataset could be used by other researchers to evaluate their performance. The
dataset will be available soon.
