Abstract
Abstract. The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.
Highlights
AND MOTIVATIONGenerating continuous 3D models of urban environments, at ground level, is becoming an increasing need for a wide range of applications
In this research work we focus on the ground surface reconstruction theme which in dense urban environments is concerned with the road, sidewalk and ramp access areas
We propose an automatic framework which combines an automatic segmentation with a high-detailed surface reconstruction framework capable to preserve sharp depth features and to generate a regular surface, while dealing with scalability issues
Summary
Generating continuous 3D models of urban environments, at ground level, is becoming an increasing need for a wide range of applications. Mobile Mapping Systems (MMS) equipped with active 3D sensors are well adapted for acquiring dense 3D measurements of the underlying surface, while driving in normal traffic conditions. Such a discrete representation must be further exploited in order to build a continuous surface via 3D modeling techniques. In this research work we focus on the ground surface reconstruction theme which in dense urban environments is concerned with the road, sidewalk and ramp access areas These sharp depth changes and geometrical details need to be preserved in order to cope with the accuracy required by the visual layer of simulator engines or for intelligent vehicles.
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