A NEW AUTOMATIC SYSTEM CALIBRATION OF MULTI-CAMERAS AND LIDAR SENSORS
- 1Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada
- 2Department of Electrical Engineering, Port-Said University, Port-Said, Egypt
Keywords: System Calibration, LIDAR, Multi-Camera, Point Cloud
Abstract. In the last few years, multi-cameras and LIDAR systems draw the attention of the mapping community. They have been deployed on different mobile mapping platforms. The different uses of these platforms, especially the UAVs, offered new applications and developments which require fast and accurate results. The successful calibration of such systems is a key factor to achieve accurate results and for the successful processing of the system measurements especially with the different types of measurements provided by the LIDAR and the cameras. The system calibration aims to estimate the geometric relationships between the different system components. A number of applications require the systems be ready for operation in a short time especially for disasters monitoring applications. Also, many of the present system calibration techniques are constrained with the need of special arrangements in labs for the calibration procedures. In this paper, a new technique for calibration of integrated LIDAR and multi-cameras systems is presented. The new proposed technique offers a calibration solution that overcomes the need for special labs for standard calibration procedures. In the proposed technique, 3D reconstruction of automatically detected and matched image points is used to generate a sparse images-driven point cloud then, a registration between the LIDAR generated 3D point cloud and the images-driven 3D point takes place to estimate the geometric relationships between the cameras and the LIDAR.. In the presented technique a simple 3D artificial target is used to simplify the lab requirements for the calibration procedure. The used target is composed of three intersected plates. The choice of such target geometry was to ensure enough conditions for the convergence of registration between the constructed 3D point clouds from the two systems. The achieved results of the proposed approach prove its ability to provide an adequate and fully automated calibration without sophisticated calibration arrangement requirements. The proposed technique introduces high potential for system calibration for many applications especially those with critical logistic and time constraints such as in disaster monitoring applications.