Detecting Changes in Urban Environments Using Terrestrial Laser Scanning
Detection of changes has been a subject for research for many years, leading to applications in a wide variety of disciplines. The challenge is performing an informed comparison when the data depict cluttered scenes such as those present in urban settings. While the use of terrestrial laser scans to detect changes may be of great value, problems associated with occlusion and sampling the scene at varying resolution become a major concern. This paper proposes a detection model which is aware of the unique features that characterize terrestrial laser scanning data and is computationally efficient. We analyze the effect of error sources associated with laser scans (e.g., returns around object boundaries) and show how an adequate representation enables a reliable and error-aware detection scheme. The application of the model on cluttered urban environments demonstrates the detection power of the proposed algorithm.
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