Visual SLAM Based on Single Omnidirectional Views

This chapter focuses on the problem of Simultaneous Localization and Mapping (SLAM) using visual information from the environment. We exploit the versatility of a single omnidirectional camera to carry out this task. Traditionally, visual SLAM approaches concentrate on the estimation of a set of visual 3D points of the environment, denoted as visual landmarks. As the number of visual landmarks increases the computation of the map becomes more complex. In this work we suggest a different representation of the environment which simplifies the computation of the map and provides a more compact representation. Particularly, the map is composed by a reduced set of omnidirectional images, denoted as views, acquired at certain poses of the environment. Each view consists of a position and orientation in the map and a set of 2D interest points extracted from the image reference frame. The information gathered by these views is stored to find corresponding points between the current view captured at the current robot pose and the views stored in the map. Once a set of corresponding points is found, a motion transformation can be computed to retrieve the position of both views. This fact allows us to estimate the current pose of the robot and build the map. Moreover, with the intention of performing a more reliable approach, we propose a new method to find correspondences since it is a troublesome issue in this framework. Its basis relies on the generation of a gaussian distribution to propagate the current error on the map to the the matching process. We present a series of experiments with real data to validate the ideas and the SLAM solution proposed in this work.