关键词: 全景相机自检校, 点线特征集成, 全景相机内方位元素检查, 移动测量系统

Abstract:

The onboard mobile mapping system is affected by pre-calibration errors, installation errors, and camera projection errors, leading to mismatches between the acquired point clouds and panoramic image sequence data. In highway scenarios, existing calibration methods face challenges due to the linear distribution of highways and the significant depth-of-field differences in panoramic images. These factors cause uneven distribution of control points with high correlation, making it difficult to adequately calibrate the camera's exterior orientation parameters, especially the translation components related to the vehicle's roll angle and travel direction. To address this issue, this paper proposes a self-calibration method for panoramic cameras in highway scenarios, integrating point-line features with depth information. The method improves the sensitivity of the adjustment model to translation components through inverse distance weighting, extracts point-line features from highway infrastructure, and constructs a joint adjustment model to reduce the impact of depth differences on uneven feature distribution, ensuring high sensitivity to errors in all directions. Experimental results show that the proposed method can accurately calibrate the exterior orientation parameters of the panoramic camera, with calibration accuracy better than 3 pixels, and the calibration model demonstrates high responsiveness to errors in all directions.

Key words: panoramic camera self-calibration, point-line feature integration, panoramic camera internal orientation elements self-check, mobile mapping system