Review of simultaneous localization and mapping (SLAM) for construction robotics applications.

With the increasing affordability of robot technologies and the reduction in size and weight of autonomous systems, the deployment of autonomous robotic systems on construction sites has gained significant attention. One major challenge faced by these systems is the accurate mapping and localization within an environment that constantly evolves on a daily basis. This computational problem, known as simultaneous localization and mapping (SLAM), has garnered substantial interest, leading to the proposal of various algorithms in recent years. This paper aims to provide a comprehensive overview of the SLAM algorithms by presenting the necessary background information, discussing the challenges involved in general, examining common approaches, and highlighting recent developments. Furthermore, the specific considerations related to deploying SLAM in construction environments are addressed. In order to provide practical insights into the advantages and use cases of state-of-the-art SLAM algorithms, a rigorous evaluation is conducted within a construction environment • Review of SLAM background, limitations, and approaches • Discussion of special considerations for localization of mobile robots in construction • Evaluation of three state-of-the-art LiDAR SLAM algorithms on a construction dataset • Experiments highlight challenges and the need for better LiDAR SLAM algorithms [ABSTRACT FROM AUTHOR]