Applications and Experiments on Query-based Object-aware Visual Language Mapping using Frame Division Method in Microcomputers

This paper demonstrates an approach to integrate object-aware map building technique which employs visual language models in microcomputers. This paper addresses the computational challenges of deploying visual language models in resource-constrained environments, such as mobile robots with microcomputers. It is achieved by separating object-aware map building process. The proposed mapping process is divided into 3 stages, data acquisition stage, object-aware map building stage, and inference stage. Experiments are conducted with Turtlebot4 mobile robot with Raspberry Pi microcomputers, to validate its performance in low-powered devices. The result showed 66% of success rate in overall text list, including 60% success rate with input texts with description. This study contributes to mobile robotics by showing that even microcomputers with limited processing power can support object-aware navigation tasks through optimized mapping method.