Abstract
In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes.
Highlights
In the last few years, automatic 3D urban cartography and modeling have gained immense interest in the scientific community, due to the ever-increasing demand for urban landscape analysis for different popular applications coupled with the recent advances in 3D data acquisition technologies
We have presented a new method for automatic 3D urban cartography that progressively removes different imperfections caused by occlusions, misclassifications of objects in the scene and ineffective incorporation of changes occurring in the environment over time, by taking advantage of incremental updating using specialized functions
Different changes occurring in the urban landscape are automatically detected and analyzed using cognitive functions of similarity, and the resulting 3D
Summary
In the last few years, automatic 3D urban cartography and modeling have gained immense interest in the scientific community, due to the ever-increasing demand for urban landscape analysis for different popular applications coupled with the recent advances in 3D data acquisition technologies. In urban environments, this task consisting in automatically generating accurate and reliable 3D cartography and models, without imperfections, using the data obtained from these hybrid terrestrial vehicles still remains a challenge. These imperfections mainly include missing features/regions, due to occlusions caused by the presence of temporarily stationary and dynamic objects (pedestrians, cars, etc.) in the scenes [1], false features resulting from misclassifications of objects in the scene [2] and failure to effectively incorporate different changes occurring in the environment over time [3]. We present a new method for automatic 3D urban cartography that progressively removes these imperfections in an effective manner
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