Fusion Of Lidar 3d Points Cloud With 2d Digital Camera Image
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Fusion of LiDAR 3D Points Cloud with 2D Digital Camera Image
Three-dimension imagery is popular these days. There are many methods for fusing multi-sensors. This thesis develops a rigid-body extrinsic calibration and registration for multi-modal sensor data fusion for 3D mapping. The time-aligned data, 3D points cloud and their intensity information from LiDAR, and texture and color from the camera, are generated by scanning the same physical scene in different manners. The range sensor and camera capture the features of fiducial targets to generate a transformation matrix. Then, two types of images, 2D image from a camera and a 3D points interpolated to a 2D intensity image, can be aligned. Registration of color information from panoramas to 3D points clouds from the LiDAR range sensor are needed to consider the correspondence between pixel coordinates of the intensity image matched with panoramas and spatial coordinates of LiDAR points. Aligning the adjacent locations one-by-one, we can generate a 3D map of a hallway in a large building. This thesis solves some of the common problems such as extrinsic calibration, scan registration, and 3D points alignment. Although this research is dedicated to indoor mapping, it can be generalized to outdoor mapping by changing to a large LiDAR sensor.
Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling
Modelling urban flood dynamics requires proper handling of a number of complex urban features. Although high-resolution topographic data can nowadays be obtained from aerial LiDAR surveys, such top-view LiDAR data still have difficulties to represent some key components of urban features. Incorrectly representing features like underpasses through buildings or apparent blockage of flow by sky trains may lead to misrepresentation of actual flood propagation, which could easily result in inadequate flood-protection measures. Hence proper handling of urban features plays an important role in enhancing urban flood modelling. This research explores present-day capabilities of using computer-based environments to merge side-view Structure-from-Motion data acquisition with top-view LiDAR data to create a novel multi-source views (MSV) topographic representation for enhancing 2D model schematizations. A new MSV topographic data environment was explored for the city of Delft and compared with the conventional top-view LiDAR approach. Based on the experience gained, the effects of different topographic descriptions were explored for 2D urban flood models of (i) Kuala Lumpur, Malaysia for the 2003 flood event; and (ii) Ayutthaya, Thailand for the 2011 flood event. It was observed that adopting the new MSV data as the basis for describing the urban topography, the numerical simulations provide a more realistic representation of complex urban flood dynamics, thus enhancing conventional approaches and revealing specific features like flood watermarks identification and helping to develop improved flood-protection measures.
Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
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