Abstract
Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency and accuracy. An effective strategy would be to frequently acquire point clouds from regular vehicles, and then take detailed measurements only where necessary. However, there are three challenges when using data from regular vehicles. First, the accuracy and density of the points are comparatively low. Second, the measurement ranges vary for different measurements. Third, tentative changes such as pedestrians must be discriminated from real changes. The method proposed in this paper consists of registration and change detection methods. We first prepare the synthetic data obtained from regular vehicles using mobile mapping system data as a base reference. We then apply our proposed change detection method, in which the occupancy grid method is integrated with Dempster–Shafer theory to deal with occlusions and tentative changes. The results show that the proposed method can detect road environment changes, and it is easy to find changed parts through visualization. The work contributes towards sustainable updates and applications of 3D road maps.
Highlights
One is acquired using mapping systems (MMS), which is used for building a 3D road map, and data of the second type is obtained from cheap sensors equipped in regular vehicles that have automatic driving or a driver supporting system
One is acquired using MMS for building the 3D road map, and another is sequential data that is routinely acquired by regular vehicles
The Iterative Closest Point (ICP) algorithm is implemented by using the Point Cloud Library (PCL) [16]
Summary
The applications of three-dimensional (3D) road maps have garnered significant attention in recent times. These maps express the geometrical shapes and structures in street environments as 3D objects. 3D road maps are expected to facilitate applications in many fields. These maps will enhance driver navigation and road management, and serve as base maps in disaster prevention and infrastructure management. It is appropriate that road maps for autonomous driving must contain information on dynamic and real-time street states, such as traffic accidents and road constructions [1]. Road maps expressing the street environment with 3D objects are important as base maps, and are being widely reseaIrScPhReSdInta. nJ.dGedo-iInsfc. u20s1s7e, 6d, .398
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