Abstract
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.
Highlights
A wide range of mapping systems integrating different sensors have been developed
This paper introduces a new technique for calibrating a system composed of multi-cameras and a LIDAR
The outline of the paper is as follows: In section 2 we describe two related topics to our work which are the construction of 3D point cloud and the Iterative Closest Point (ICP)
Summary
A wide range of mapping systems integrating different sensors have been developed. Different indirect techniques have been proposed depending on simple targets, see for example (Kwak et al, 2011) These techniques require the collection of high number of images from different ranges which might not be always easy to achieve specially for time constrained applications. It is important to note that the proposed technique has two main advantages: (a) the simplicity of the used target which enables a more convenient calibration, and (b) Avoiding the need for precise point to point correspondence, as the registration step can be effectively employed without such point to point correspondence This proposed calibration approach depends on matching the two 3D point clouds created from the LIDAR and cameras instead of matching specific points which might need more effort.
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