In the present work we focus on augmented

In the present work, we focus on augmented reality (AR), which can super-impose a present surrounding landscape acquired with a video camera and 3DCG (Milgram and Kishino, 1994; Azuma, 1997). The use of AR involves the inclusion of a landscape assessment object in the present surroundings. In this buy ketotifen fumarate case , a landscape preservation study can be performed. A number of outdoor ARs have been used for environmental assessment and for the pre-evaluation of the visual impact of large-scale constructions on landscape (Rokita, 1998; Reitmayr and Drummond, 2006; Ghadirian and Bishop, 2008; Wither et al., 2009; Yabuki et al., 2012). With the use of the proposed system, the time and cost needed to perform a 3DCG modeling of present surroundings are drastically reduced. In AR, the realization of registration accuracy with a live video image of an actual landscape and 3DCG is still an important feature (Charles et al., 2010; Ming and Ming, 2010; Schall et al., 2011). AR registration methods are roughly categorized as follows: 1) use of physical sensors such as the global positioning system (GPS) and gyroscope sensors, 2) use of an artificial marker, and 3) feature point detection method (Neumann and You, 1999). The first method generally requires expensive hardware to realize highly precise and accurate registration (Feiner et al., 1997; Thomas et al., 1998; Behzadan et al., 2008; Leon et al., 2009; Schall et al., 2009; Watanabe, 2011). The use of this method is thus problematic because the required equipment may not always be available for the users. The second method achieves registration accuracy using an inexpensive artificial marker (Kato and Billinghurst, 1999). However, an artificial marker must always be visible to the AR camera. This condition limits user mobility. Moreover, a large artificial marker is needed to realize high precision (Yabuki et al., 2011). The third method involves the extraction of feature points and has gained momentum in the field of research, including in the topic of registration for outdoor AR (Klein and Murray, 2007; Ventura, 2012). However, the proposed system was verified on the assumption that the distance between the AR camera and the target of landscape simulation is less than 100m (Ventura and Höllerer, 2012). This distance is treated as a near view area in the aspect of landscape study. In the present work, we consider an AR system that allows a distant view area. Hence, the distance between the AR camera and the target of landscape simulation is considered and set to 2000m. Smartphones or tablet computers are widely available as handheld devices. Several studies on handheld AR systems have been reported for indoor and short-range outdoor use (Wagner and Schmalstieg, 2003; Damala et al., 2008; Schall et al., 2008; Mulloni et al., 2011). One product of these studies is the sensor-oriented mobile AR (SOAR) system, which realizes registration accuracy using GPS, a gyroscope sensor, and a video camera that are mounted in a handheld smartphone (Fukuda et al., 2012; see Chapter 2). The SOAR system can easily be used in landscape simulation. However, the position information obtained by GPS, particularly in urban areas, is relatively low. Therefore, the present study develops a 3D map-oriented handheld AR system (hereinafter referred to as 3DMAP-AR) to obtain highly precise position information using a simple operation. To obtain position data, the proposed system achieves geometric consistency using a 3D map instead of GPS. The system also comprises a gyroscope sensor to obtain posture data and a video camera to capture live video. All these components are mounted in a smartphone. Registration accuracy is evaluated to simulate an urban landscape from a short- to a long-range scale. The latter involves a distance of approximately 2000m. An inexpensive AR system with high accuracy and flexibility is realized through this research.
Developed SOAR and 3DMAP-AR