The composition of multiple-layer Light Detection And Ranging (LiDAR) and camera is commonly used in autonomous perception systems. Complementary information of these sensors is instrumental in the reliable surrounding perception. However, it is a difficult work for obtaining the extrinsic parameters between LiDAR and camera, which must be known for some perception algorithms. In this study, we present a method, using only three 3D-2D correspondences to compute the extrinsic parameters between Velodyne-VLP16 LiDAR and monocular camera. The procedure is that 3D and 2D features are extracted respectively from the point cloud and image of a custom calibration target and then the extrinsic parameters are obtained based on these features by the perspective-3-point (P3P) algorithm. Outliers with minimum energy at geometrical discontinuities of target are used as control points for extracting key features in LiDAR point cloud. Moreover, a novel method is presented to distinguish the correct solution from multiple P3P solutions. The method depends on conic shape discrepancies in spaces of the different solutions.
As a signal processing theory, compressive sensing (CS) breaks through the limitations of the traditional Nyquist sampling theorem and provides the possibility to solve the high sampling rate, large data volume, and real-time processing difficulties of traditional high-resolution radar. Based on the theory of single-pixel cameras, an array detection imaging system is built, and main structural parameters are analyzed. The simulation experiment of a simple target is organized to show that the number of measurements can be reduced by achieving the parallel operation through increasing the number of detectors. When the target changes, it is found that the sparsity problem has a great influence on the number of measurements. Therefore, an improved method is proposed using the structure flexibility of fiber array and detectors, which can reduce the number of measurements simultaneously while decreasing the number of detectors, which is superior to the original method.
Although the streak tube imaging lidar(STIL) is widely applied in target recognition and imaging, combining the compressive sensing(CS) theory with it has only just begun. To the best of our knowledge, most studies on this combination theory are about ultra-fast imaging. We harness the advantages of streak tube and CS to provide a novel idea in three-dimensional imaging. The imaging system model is built, and mainly structures are introduced such as fiber array and digital micromirror device(DMD). Simulation experiments are organized. In the process of reconstructing the intensity image and range image of the target, the extraction methods of measurement matrix required by the CS algorithm are given respectively.
Streak tube imaging lidar has been widely applied in target recognition and imaging because of its high accuracy and frame rate. Compressed ultrafast photography technique employs a digital micromirror device (DMD) and a streak camera. It is developed to satisfy the requirements of imaging of ultrafast processes. The concept of structure provides a new direction for three-dimensional (3-D) imaging. This paper studies the streak tube 3-D imaging system based on compressive sensing (CS) from the perspective of imaging system construction and image reconstruction algorithms. The system model is built, and mainly structures are introduced such as the fiber array and DMD. Two simulation experiments are organized. First, the stripe images of a simple target are obtained. In the process of reconstructing the intensity image and range image, the extraction methods of the measurement matrix required by the CS algorithm are given, respectively. The resulting images and variance curve show that the image quality increases with the number of measurements. The second experiment with a complex target is carried out. Two levels of distance interval are used to analyze the imaging effect in the simulation. It is found that the image resolution is directly related to the distance interval selection.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.