# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 2 Related Works
### Omnidirectional-Vision SLAM  
Recently, many vision-based SLAM algorithms have been proposed [9]. According to the FOV of the cameras, these approaches can be grouped into two categories: one is based on perspective images with conventional limited FOV, and the other is an approach that relies on omnidirectional images with hemispherical FOV. Here, we focus the discussion of the related work on those omnidirectional SLAM approaches.  
One of the most popular techniques for SLAM with omnidirectional cameras is the extended Kalman filter (EKF). For example, [PERSON] et al. [10] integrated the spherical camera model into the EKF-SLAM by linearizing direct and the inverse projection. Developed in [10], [PERSON] et al. [11] introduced a new computation for the descriptor patch for catadioptric omnidirectional cameras that aimed to reach rotation and scale invariance. Then, a new view initialization mechanism was presented for the map building process within the problem of EKF-based visual SLAM [12]. [PERSON] et al. [13] proposed a SLAM algorithm (OV-FastSLAM) for omniVision cameras operating with severe occlusions. [PERSON] et al. [14] presented an approach which was applied to SLAM that addressed qualitative loop closure detection using a spherical view. [PERSON] et al. [15] proposed an extension of LSD-SLAM to a generic omnidirectional camera model. The resulting method was capable of handling central projection systems such as fish-eye and catadioptric cameras.  
In the above omnidirectional-vision SLAM, although they used omnidirectional images, their images could still not obtain a view as wide as that of full-view images. Additionally, since processing such as feature extraction and feature matching was not conducted based on the spherical model, they used perspective projection. These approaches are not considered a spherical-model-based approach, according to a recent survey [16].  
However, visual odometry and structure from motion are also designed for estimating camera motion and 3D structure in an unknown environment. There are some camera pose estimation methods for full-vision systems were proposed [17]. An extrinsic camera parameter recovery method for a moving omni-directional multi-camera system was proposed; this method is based on using the shape-from-motion and the PnP techniques [18]. [PERSON] et al. investigated the use of full spherical panoramic images for Structure form Motion algorithms; several error models for solving the pose problem, and the relative pose problem were introduced [19]. [PERSON] et al. obtained a 3D metric reconstruction of a scene from two highly-distorted omnidirectional images by using image correspondence only [20]. Our spherical model is similar to theirs; their methods also prove that wide-view based on the spherical model has its advantages over limited-view.  
The aim of this research is to develop a SLAM method for a mobile robot in indoor environments. The proposed method is based on a spherical model. The problem of tracking failure due to limitedview can be completely avoided. Furthermore, as described in Section 1, full-view images also result in better performance for SLAM. The processing of the proposed full-view SLAM method is based on a spherical camera model. The effectiveness of the proposed method is shown using real-world experiments in indoor environments. Additionally, an experiment is also conducted to prove that the accuracy is affected by the view field.