Evaluation of SLAM algorithms for search and rescue applications

Search and rescue robots have been widely investigated to detect humans in disaster scenarios. SLAM (Simultaneous Localisation and Mapping), as a critical function of the robot, can localise the robot and create the map during the rescue tasks. In this paper, prominent 2D SLAM algorithms are investigated and three of them (Gmapping, Hector, and Karto) are implemented on a low-cost search and rescue robot to demonstrate their feasibility. Moreover, experiments containing various ground surface scenarios are performed. Maps created by various SLAM algorithms are compared to identify the best SLAM algorithm for search and rescue tasks using a low-cost robot. The experimental results suggest that Karto SLAM performs best for low-cost search and rescue robots among the three SLAM algorithms.