Abstract
In this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors’ coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors’ geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction. The proposed methods have been validated with synthetic and real data.
Highlights
Outdoor 3D reconstruction is a challenging aspect in many applications, such as mapping, autonomous navigation and localization, disaster control and many others
The radar and the camera were mounted in a fixed configuration on the top of a vehicle, in front of the scene
The results show a realistic error for the 3D reconstruction of targets at a mean depth of 12 m
Summary
Outdoor 3D reconstruction is a challenging aspect in many applications, such as mapping, autonomous navigation and localization, disaster control and many others. Methods existing in the literature are based on vision or range sensors or a combination of these two sensors In this regard, a combination of sensors is an obvious solution to overcome the limitations of single sensors. Outdoor 3D reconstruction is a challenging aspect because of many limitations due to large-scale and unshaped features and bad illumination conditions. For these reasons, the proposal of a simple, robust and fast algorithm dedicated to complete such an objective represents a major interest for several applications. The authors in [6] provide a comprehensive overview of urban reconstruction
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