Extrinsic Camera Parameter Estimation Research Articles

특징점 병합과 카메라 외부 파라미터 추정 결과를 고려한 B-snake기반 차선 검출

This paper proposes a robust lane detection algorithm for bumpy or slope changing roads by estimating extrinsic camera parameters, which represent the pose of the camera mounted on the car. The proposed algorithm assumes that two lanes are parallel with the predefined width. The lane detection and the extrinsic camera parameter estimation are performed simultaneously by utilizing B-snake in motion compensated and merged feature map with consecutive sequences. The experimental results show the robustness of the proposed algorithm in various road environments. Furthermore, the accuracy of extrinsic camera parameter estimation is evaluated by calculating the distance to a preceding car with the estimated parameters and comparing to the radar-measured distance.

Real-time and accurate extrinsic camera parameter estimation using feature landmark database for augmented reality

In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.

1A2-Q15 Mobile Mapping Systemにおける広域三次元計測のためのレーザスキャナを利用したカメラ外部パラメータの推定(ITSとロボット・メカトロ技術)

This paper describes an extrinsic camera parameter estimation method for a 3D measurement using a mobile mapping system (MMS). MMS can acquire the 3D information of the road environment efficiently using the laser scanner. However, a measurement range is limited because of the performance of the laser scanner, the use of camera for the 3D measurement is expected. In the 3D measurement using the camera, the accurate extrinsic camera parameter estimation is required. We propose an automatic camera parameter estimation method that uses the measurement data of laser scanner acquired by the MMS. In the result of experiment, it is confirmed that the proposed method can estimate the extrinsic camera parameter to an accuracy of 0.07 degree.