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The Streak-Tube Imaging Lidar (STIL) has been widely used in high-precision measurement systems due to its ability to capture detailed spatial and temporal information. In this paper, we proposed a ranging measurement method that integrates a Time-to-Digital Converter (TDC) with a streak camera in a remote STIL system. In this method, the TDC accurately measures the trigger pulse time, while the streak camera captures high time-resolution images of the laser echo, thereby enhancing both measurement accuracy and range. A corresponding ranging model is developed for this method. To validate the system’s performance, an outdoor experiment covering a distance of up to 6 km was conducted. The results demonstrate that the system achieved a distance measurement accuracy of 0.1 m, highlighting its effectiveness in long-range applications. The experiment further confirms that the combination of STIL and TDC significantly enhances accuracy and range, making it suitable for various long-range, high-precision measurement tasks.

Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m.

Owing to the special working systems of streak tube imaging lidar (STIL), the time and space dimensions are coupled together on the streak images. This coupling can cause measurement errors in 3D point clouds and can make measurement results more complicated to calibrate than other kinds of lidars. This paper presents a method to generate a time calibration array and an angle calibration array to separate the offset of the streak into time dimension and space dimension. The time and space information of the signal at any position on the streak image can be indexed through these two arrays. A validation experiment on aircraft was carried out, and the range error of the 3D point cloud was improved from 0.41 m to 0.27 m using the proposed calibration method. Thus, using the proposed calibration method can improve the accuracy of the point cloud produced by STIL.

In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.