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Tasks such as city modeling or urban planning require the registration, alignment, and comparison of multi-view and/or multi-temporal remote sensing data. ALS (airborne laser scanning) is one of the established techniques to deliver these data. Regrettably, direct georeferencing of ALS measurements usually leads to considerable displacements that limit connectivity and/or comparability of overlapping point clouds. Most reasons for this effect can be found in the impreciseness of the positioning and orientation sensors and their misalignment to the laser scanner. Typically, these sensors are comprised of a GNSS (global navigation satellite system) receiver and an IMU (inertial measurement unit). This paper presents a method for the automatic self-calibration of such ALS systems and the alignment of the acquired laser point clouds. Although applicable to classical nadir configurations, a novelty of our approach is the consideration of multiple data sets that were recorded with an oblique forward-looking full-waveform laser scanner. A combination of a region-growing approach with a RANSAC (random sample consensus) segmentation method is used to extract planar shapes. Matching objects in overlapping data sets are identified with regard to several geometric attributes. A new methodology is presented to transfer the planarity constraints into systems of linear equations to determine both the boresight parameters and the data alignment. In addition to system calibration and data registration, the presented workflow results in merged 3D point clouds that contain information concerning rooftops and all building facades. This database represents a solid basis and reference for applications such as change detection.

ABSTRACT:Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95 % of the measurements are labeled correctly.

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

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