# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 1 Introduction  
For a mobile robot, finding its location and building environment maps are basic and important tasks. An answer to this need is the development of simultaneous localization and mapping (SLAM) methods. For vision-based SLAM systems, localization and mapping are achieved by observing the features of environments via a camera. Therefore, the performance of a vision-based SLAM method depends not only on the algorithm, but also the feature distribution of environments observed by the camera. Figure 1 shows a sketch of an indoor environment. For such a room, there may be few features within the field of view (FOV). Observing such scenes with a limited FOV camera, the SLAM often fails to work. However, these problems can be avoided by using a full-view camera.  
Another reason for using a full-view camera is that it improves the accuracy of SLAM methods. Because some different motions may have similar changes in an image of a limited FOV camera, these motions are discriminated with a full-view image. For example, as shown in Figure 2, the translation of a limited-view camera along the horizontal axis or rotation in the horizontal direction may result in the same movement for some features on images, i.e., Target 1 in Figure 2. However, for Targets 2-4, the camera motion causes different movements. This means that in some cases, different motions may be difficult to decouple from the observation of limited FOV images, while it is possible to distinguish from the observation of full-view images.  
Based on the above observations, a vision-based SLAM method of using full-view images can effectively manage sparse-feature or partially non-feature environments, and also achieve higher accuracy in localization and mapping than conventional limited field-of-view methods. Until now, few SLAM methods for using omnidirectional images have been proposed, and although a wider view results in a better performance in localization and mapping, there are no experiments assessing how accuracy is affected by the view field.  
In this paper, we realize simultaneous localization and mapping (SLAM) on full-view images. The principle is similar to the typical approach of the conventional SLAM methods, PTAM (parallel tracking and mapping) [1]. In the proposed method, a full-view image is captured by Ricoh Theta [2]. Next, feature points are extracted from the full-view image. Then, spherical projection is used to compute projection and back-projection of the scene points. Finally, feature matching is performed using a spherical epipolar constraint. The characteristic of this paper is that a spherical model is used throughout processing, from feature extracting to localization and mapping computing.  
The rest of this paper is organized as follows. In the next section, we introduce the related research. In Section 3, we introduce a camera model. Section 4 describes our system. Comprehensive validations are shown in Section 5. Finally, we summarize our work in Section 6.  
Figure 1: The performance of a vision-based SLAM is influenced by the field of view.  
Figure 2: The translation of a limited-view camera along the horizontal axis and the horizontal rotation may result in the same movement for some features in images (Target 1); however, the same camera motion causes different movements for the features in other places (Target 2–4), only a full-view camera can capture them.