An Automatic Calibration Method for AVM Cameras

Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

“…Ref. [ 210 ] presents automatic AVM camera calibration using image processing and machine learning, streamlining the process without a physical calibration rig. Ref.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

“…Despite the fact that numerous approaches [ 8 , 9 , 10 , 11 , 12 ] have been explored to achieve accurate bird’s eye view images, they still exhibit several limitations, including limited quantitative comparison between features observed in AVM and features in real-world environments and substantial efforts for collecting data. Alternatively, in [ 5 , 6 ], they avoided the influence of distortion errors by utilizing an additional Inertial Measurement Unit (IMU) sensor based on a pre-built map or leveraging an externally provided High Definition (HD) vector map.…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed in [ 9 ] aims to calibrate an AVM image by matching the gradient and position of the lane observed from the front and rear cameras with that seen from the left and right cameras. On the other hand, the method proposed in [ 10 ] focuses on calibrating the AVM image to ensure the detected parking lines are parallel or perpendicular to the vehicle. However, since these methods only perform relative comparisons between the images of each camera and do not quantitatively compare the AVM image with the real environment, it may not be considered as a complete solution to address AVM distortion errors.…”

Section: Introductionmentioning

confidence: 99%

Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)

Lee,

Kim,

Ahn

et al. 2023

Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches.

“…Lee et al [30] calibrated AVM cameras using only two circle-shaped calibration boards. This method takes multiple photos while the vehicle passes between the two calibration boards to achieve the effect of having more calibration boards placed.…”

Section: Introductionmentioning

confidence: 99%

“…These calibration methods must repeat the road lane detection process un integrity of the detected lanes is verified. The methods we have surveyed indicat camera calibration without the use of calibration boards can face various challenges Lee et al [30] calibrated AVM cameras using only two circle-shaped calib boards. This method takes multiple photos while the vehicle passes between th calibration boards to achieve the effect of having more calibration boards placed.…”

Section: Introductionmentioning

confidence: 99%

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Lee

2021

An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size.