Abstract
Abstract. This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone’s motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.
Highlights
The demand for dense 3D point clouds has been increasing over the past decade
The Interior Orientation Parameters (IOPs) can be obtained from the camera calibration process, while Exterior Orientation Parameters (EOPs) can be obtained using one of two geo-referencing approaches in photogrammetry; indirect and direct
The indirect approach uses a set of control points to determine EOPs, while the direct approach uses on-board GPS/INS position and orientation systems for calculating EOPs at the time of exposure, using Mobile Mapping System (MMS) (El-Sheimy, 2008)
Summary
The demand for dense 3D point clouds has been increasing over the past decade. This demand is to satisfy a variety of applications, including 3D object reconstruction and mapping. An active system has the ability to acquire a precise and reliable 3D point cloud of an object directly (i.e., with a laser scanner) This system is expensive when compared to a passive system. Current generation of smartphones are equipped with micro-electro mechanical systems (MEMS) based navigation sensors (such as gyroscopes, accelerometers, magnetic compass, and barometers), offering the potential for integrating these sensors with GPS for outdoor applications (e.g. the IPhone integrates 3-accelerometers, 3gyroscopes, pedometer, compass, barometer, step detector, and step counter). The objective of this paper is to explore the feasibility of using consumer-grade smartphones for direct geo-referenced 3D point cloud generation from overlapping imagery
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