Abstract
RGB-D sensors capture RGB images and depth images simultaneously, which makes it possible to acquire the depth information at pixel level. This paper focuses on the use of RGB-D sensors to construct a visual map which is an extended dense 3D map containing essential elements for image-based localization, such as poses of the database camera, visual features, and 3D structures of the building. Taking advantage of matched visual features and corresponding depth values, a novel local optimization algorithm is proposed to achieve point cloud registration and database camera pose estimation. Next, graph-based optimization is used to obtain the global consistency of the map. On the basis of the visual map, the image-based localization method is investigated, making use of the epipolar constraint. The performance of the visual map construction and the image-based localization are evaluated on typical indoor scenes. The simulation results show that the average position errors of the database camera and the query camera can be limited to within 0.2 meters and 0.9 meters, respectively.
Highlights
The emergence of wireless communication and the Global Positioning System (GPS) has ignited the idea of Personal Navigation Systems (PNSs)
Most have concentrated on radio signal-based methods, making use of radio access networks. These algorithms are mainly based on measuring the distances to access points by means of the angle of arrival (AOA), the time of arrival (TOA), the carrier phase of arrival (POA), or the received signal strength indicator (RSSI), and so forth [1, 2]
According to functions and sizes, these rooms are divided into four scene classes, namely, the small room scene, the medium
Summary
The emergence of wireless communication and the Global Positioning System (GPS) has ignited the idea of Personal Navigation Systems (PNSs). Because satellite signals cannot penetrate buildings, a smartphone with a GPS sensor is incapable of providing reliable position services to a pedestrian. Many indoor localization algorithms have been proposed and implemented Of these algorithms, most have concentrated on radio signal-based methods, making use of radio access networks. Most have concentrated on radio signal-based methods, making use of radio access networks These algorithms are mainly based on measuring the distances to access points by means of the angle of arrival (AOA), the time of arrival (TOA), the carrier phase of arrival (POA), or the received signal strength indicator (RSSI), and so forth [1, 2]. The effective range of Bluetooth signals is approximately 5–10 meters, which leads to frequent beacon switching and results in high power consumption
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