Performance Evaluation of ROS-Based SLAM Algorithms for Handheld Indoor Mapping and Tracking Systems

Simultaneous localization and mapping (SLAM) is an important field of work not only in robotics, but also in mobile platforms. This research work provides insights into how SLAM techniques are deployed in an indoor environment to aid first responders with their duties. Due to the hazardous nature of the environment and the need for sensitivity due to the potential involvement of human subjects, autonomous robots cannot be used. So, the first responders must carry the scanning equipment and perform SLAM at the same time. As a result, unlike standard robot platforms, there will be no reliable odometry source, and SLAM will have to deal with the user’s unpredictable movement. In this work, we compare and examine robotic operating system (ROS)-based SLAM approaches without using any odometry for their application in the above-mentioned circumstances. Gmapping, HectorSLAM, and Cartographer have been chosen as the candidates for this evaluation. We evaluated these approaches in two different environments: a lab office and a long corridor. The research results show that Cartographer outperforms the other two techniques in our test setup in terms of map quality and trajectory tracking. The Cartographer’s mapping error ranged from 0.017 to 0.3548 m.