Real-Time Artificial Intelligence Based Visual Simultaneous Localization and Mapping in Dynamic Environments - a Review

This paper aims to review current approaches for real-time artificial intelligence-based visual Simultaneous Localization and Mapping (vSLAM) operating in dynamic environments. Through the review of several relatively recent published papers for vSLAM in dynamic environments, an attempt is made to explain the concept of Simultaneous Localization and Mapping (SLAM) and its purpose; identify the general framework for real-time AI-based vSLAM approaches in dynamic environments and highlighting the potential solutions that has been developed and their significant results. All related information regarding this topic was obtained with the intention to answer three main questions. Firstly, how do robots localize and map within an unknown environment? Secondly, how can state-of-the-art vSLAM be modified with an AI algorithm to function within a dynamic environment? And lastly, what level of success has these approaches achieved in developing methods for real-time AI-based vSLAM in dynamic environments? The paper intends to provide readers with a clearer general understanding of SLAM and acts as a road map for steps to move forward in developing viable approaches for real-time vSLAM in dynamic environments based on artificial intelligence.

Automated summary

This paper reviews current approaches for real-time artificial intelligence-based visual Simultaneous Localization and Mapping (vSLAM) in dynamic environments. It explains the concept and purpose of Simultaneous Localization and Mapping (SLAM), identifies the general framework for real-time AI-based vSLAM in dynamic environments, and highlights the potential solutions that have been developed.