Abstract
In this paper we present an approach to the Simultaneous Localization and Mapping (SLAM) problem using a team of autonomous vehicles equipped with vision sensors. The SLAM problem considers the case in which a mobile robot is equipped with a particular sensor, moves along the environment, obtains measurements with its sensors and uses them to construct a model of the space where it evolves. In this paper we focus on the case where several robots, each equipped with its own sensor, are distributed in a network and view the space from different vantage points. In particular, each robot is equipped with a stereo camera that allow the robots to extract visual landmarks and obtain relative measurements to them. We propose an algorithm that uses the measurements obtained by the robots to build a single accurate map of the environment. The map is represented by the three-dimensional position of the visual landmarks. In addition, we consider that each landmark is accompanied by a visual descriptor that encodes its visual appearance. The solution is based on a Rao-Blackwellized particle filter that estimates the paths of the robots and the position of the visual landmarks. The validity of our proposal is demonstrated by means of experiments with a team of real robots in a office-like indoor environment.
Highlights
Mobile robots must possess a basic skill: the ability to plan and follow a path through the environment in an optimal way, while avoiding obstacles and computing its location within the map
It is worth noting that Simultaneous Localization and Mapping (SLAM) algorithms focus on the incremental construction of a map, given a set of movements carried out by the robots and the set of observations obtained from different locations, and they do not consider the computation of the movements that need to be performed by the robots
We propose a method to solve the data association based on visual information that is well suited for the visual SLAM algorithm presented here
Summary
Mobile robots must possess a basic skill: the ability to plan and follow a path through the environment in an optimal way, while avoiding obstacles and computing its location within the map. It is worth noting that SLAM algorithms focus on the incremental construction of a map, given a set of movements carried out by the robots and the set of observations obtained from different locations, and they do not consider the computation of the movements that need to be performed by the robots This is generally considered as a different problem, denoted as exploration. We propose a method to solve the data association based on visual information that is well suited for the visual SLAM algorithm presented here This algorithm has been validated in indoor environments using real data obtained by a network of vision sensors installed on mobile robots.
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