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Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

In this contribution, we present a simple and intuitive approach for estimating the exterior (geometrical) calibration of a Lidar instrument with respect to a camera as well as their synchronization shifting (temporal calibration) during data acquisition. For the geometrical calibration, the 3D rigid transformation of the camera system was estimated with respect to the Lidar frame on the basis of the establishment of 2D to 3D point correspondences. The 2D points were automatically extracted on images by exploiting an AprilTag fiducial marker, while the detection of the corresponding Lidar points was carried out by estimating the center of a custom-made retroreflective target. Both AprilTag and Lidar reflective targets were attached to a planar board (calibration object) following an easy-to-implement set-up, which yielded high accuracy in the determination of the center of the calibration target. After the geometrical calibration procedure, the temporal calibration was carried out by matching the position of the AprilTag to the corresponding Lidar target (after being projected onto the image frame), during the recording of a steadily moving calibration target. Our calibration framework was given as an open-source software implemented in the ROS platform. We have applied our method to the calibration of a four-camera mobile mapping system (MMS) with respect to an integrated Velodyne Lidar sensor and evaluated it against a state-of-the-art chessboard-based method. Although our method was a single-camera-to-Lidar calibration approach, the consecutive calibration of all four cameras with respect to the Lidar sensor yielded highly accurate results, which were exploited in a multi-camera texturing scheme of city point clouds.