This paper presents a system designed to develop a high-resolution map of public roads by capturing high-resolution surface images. Unlike conventional system, the proposed system applies a field programmable gate array (FPGA) to synchronize camera, strobe light, IMU, and GPS by using FPGA’s high clock frequency and flexibility to multiple devices. The proposed system, which can be mounted on a regular vehicle, contains a Complementary Metal–Oxide–Semiconductor (CMOS) camera which can achieve 0.006 ms shutter speed and 150 fps frame rate. This camera’s high shutter speed and high frame rate can help capturing images with overlapping region at fast driving speed so that no area is missing from road surface image capturing. The images collected by the proposed system contain clear patterns without motion blur, and feature points on images and feature matching can be found by using existing algorithm like Scale-invariant feature transform (SIFT) between any two consecutive images. Camera’s vertical facing towards road surface enables using affine transformation to reduce computational time of registration by calculating only six instead of eight parameters of using homography matrix. IMU and GPS data are synchronized and recorded together with image capturing, and is applied to reduce accumulated error for long-distance mapping by providing motion model and location information of vehicle for image registration process. A clear millimeter-resolution map obtained at up to 100 km/h is uploaded to Google map together with extracted road features as database, which can be used as visual landmarks to help self-driving vehicle getting more environmental information and correcting its localization. In this paper, the effectiveness of this system and mapping method is illustrated qualitatively and quantitatively through experimental evaluation.