Evolution of Simultaneous Localization and Mapping Framework for Autonomous Robotics—A Comprehensive Review

Abstract

Autonomous robotics plays a pivotal role to simplify humanmachine interaction while meeting the current industrial demands. In that process, machine intelligence plays a dominant role during the decision making in the operational state-space. Primarily, this decision making and control mechanism relies on sensing and actuation. Simultaneous localization and mapping (SLAM) is the most advanced technique that facilitates both sensing and actuation to achieve autonomy for robots. This work aims to collate multi-dimensional aspects of simultaneous localization and mapping techniques primarily in the purview of both deterministic and probabilistic frameworks. This investigation on SLAM classification is further elaborated into different categories such as Feature-based SLAM and Optimization based SLAM. In this work, the chronological evolution of SLAM technique develops a comprehensive understanding among the concerned research community.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…Along this journey, different V-SLAM methods have been created to tackle specific challenges in robot navigation, mapping, and understanding the surroundings ( Aloui et al, 2022 ; Sun et al, 2017 ). To verify and compare these V-SLAM methods, important datasets have been created which played a crucial role in the field ( Pal et al, 2022 ; Tian et al, 2023a ). In this section, we explore the evolution of V-SLAM methods over time and how they have advanced with the help of using the suitable datasets.…”

“…Furthermore, it provides real-time capabilities, allowing robots to make decisions on-the-fly without relying on pre-existing maps. Its utility extends to the extraction, organization, and comprehension of information, thereby enhancing the robot’s capacity to interpret and interact effectively with its environment ( Pal et al, 2022 ; Lee et al, 2020 ; Aslan et al, 2021 ). It is crucial to enable these robots to autonomously navigate and interact in human environments, thus reducing human effort and enhancing overall productivity ( Arfa, 2022 ).…”