SLAM; definition and evolution

Simultaneous Localization and Mapping (SLAM) is a key problem in the field of Artificial Intelligence and mobile robotics that addresses the problem of localization and mapping when a prior map of the workspace is not accessible. The determination of the SLAM problem has gained significant research momentum up to recent times. In this paper, firstly the problem of SLAM, its general model, framework, the difficulties, and leading approaches are described. Secondly, the progress of SLAM solving algorithms is surveyed throughout history. Pre-development, early SLAM solving algorithms, recent and present methods are presented and the progression of the state-of-art is reviewed based on the impact of leading approaches. We have selected some of the most important approaches of all time (1986–2019) to understand the research development, current trends, and intellectual structure of SLAM. Furthermore, from the trend of recent studies and the existence of difficult problems, a brief but sufficient review in the visual SLAM with the most outstanding approaches is presented. This paper provides one single sufficient review that allows researchers to understand the trend of SLAM, where it has come from, where it is going to and what needs to be more investigated in the SLAM-related field area. The future, in other words, the potential most important approaches inspiring the future researches in the SLAM problem can be seen. This paper will be an efficient overview and a valuable survey for introducing the SLAM solving approaches in mobile robotics as well as the general application of SLAM.