Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS)

• Main Authors: Vijay Kakani, Hakil Kim, Mahendar Kumbham, Donghun Park, Cheng-Bin Jin, Van Huan Nguyen
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Sensors
• Subjects: advanced driver-assistance system (ADAS); larger field-of-view (FOV); self-calibration; radial distortions; parameter sharing; model-specific empirical γ-residual rectification factor
• Online Access: https://www.mdpi.com/1424-8220/19/15/3369

This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg&#8722;Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical <inline-formula> <math display="inline"> <semantics> <mi>&#947;</mi> </semantics> </math> </inline-formula>-residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.