Abstract
This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution.
Highlights
Over the past two decades, mobile mapping systems (MMS) have been a vital source of direct geo-referenced data, which can be used for a variety of applications (e.g., mapping, 3D modeling, highway inventory, engineering projects, and Geographic Information System (GIS) data updates). land-based MMS is one of the main sources for acquiring direct geo-referenced data, there are many drawbacks to using the current MMS (i.e., their large size and complexity, as well as its high cost due to the use of expensive Inertial Measurements Unit (IMU) and global navigation satellite System (GNSS) receivers) which have restricted their widespread adoption in the survey and mapping industries
To test the accuracy of the proposed methodologies, the reference Exterior Orientation Parameters (EOPs) of the images and the Object Points (OPs) were measured using Total Station, whereby the position of each camera station was determined within 1 cm and the rotation angles were within 30 arc second accuracies based on the Total Station’s measurement accuracy
This paper introduced a robust smartphone-based MMS in conjunction with a new proposed workflow that overcomes the drawbacks of random errors associated with the smartphone motion sensors and poor GPS accuracy
Summary
Over the past two decades, mobile mapping systems (MMS) have been a vital source of direct geo-referenced data, which can be used for a variety of applications (e.g., mapping, 3D modeling, highway inventory, engineering projects, and Geographic Information System (GIS) data updates). Land-based MMS is one of the main sources for acquiring direct geo-referenced data, there are many drawbacks to using the current MMS (i.e., their large size and complexity, as well as its high cost due to the use of expensive Inertial Measurements Unit (IMU) and GNSS receivers) which have restricted their widespread adoption in the survey and mapping industries. The market for land-based MMS is small, and the existing MMS typically are operated by the companies or institutions that built them, which means that their more efficient data collection is not available for wider use [1]. The accuracy of direct geo-referenced data depends on the intended. This paper focuses on the development of a low-cost MMS based on smartphone technology
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