Publication

Abstract : Autonomous navigation via GMapping-based SLAM (Simultaneous Localization and Mapping) enables robots to both navigate and map their surrounding environment. This paper conducts a comprehensive evaluation of the performance of the GMapping-based SLAM algorithm within the realm of autonomous robotic navigation. A two-wheel robot equipped with GMapping-based SLAM undergoes testing in dynamic conditions to assess its real-world performance. The focus lies on enhancing our understanding of the algorithm's reaction to dynamic conditions, investigating its response to fast-moving objects, its adaptation to sudden environmental changes, and its mapping efficiency. Through a meticulously designed series of experiments, the GMapping algorithm is scrutinized under diverse conditions to gauge its robustness and adaptability. During the experiments, the robot encounters fast-moving obstacles at varying speeds, observing its mapping and path-planning capabilities. Specifically, a fast-moving obstacle is simulated using a remotely operable TurtleBot with speeds ranging from 0.2 to 0.6 m/s. The assessment process involves metrics such as velocity, speed, and distance to the fast-moving obstacle as primary indicators. The findings from these experiments offer insights into how GMapping SLAM performs in sudden environmental conditions. This research acts as a foundation for advancing GMapping's capabilities and facilitating the evolution of intelligent robotic platforms in complex real-world environments.