Abstract
This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.
Highlights
A mobile robot is an automatic machine that is capable of movement in any given environment
In this paper we have presented a novel taxonomy of vision systems for ground mobile robot (GMR)
The taxonomy proposed is intended to facilitate the identification of the main topics related to robotic vision systems
Summary
A mobile robot is an automatic machine that is capable of movement in any given environment. The robot incorporates a set of sensors to perceive the environment and either makes decisions autonomously or pass the information on to a remote human operator who controls the robot via teleoperation. While a teleoperated GMR relies on humans for decision-making, autonomous robots need to incorporate artificial intelligence (AI) capabilities to perform this process In this sense, AI has been roughly divided into two schools of thought since its beginnings: symbolic and sub-symbolic. In these kinds of tasks, an accurate environmental representation is needed.
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