Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction

Abstract

Wheeled lifting robots have found widespread applications in various industrial and logistical environments. However, traditional robots are far from adequate in terms of visual perception capabilities. Additionally, their remote control methods suffer from inefficiencies, which tend to bring safety concerns. To address these issues, this work proposes an autonomous multi-sensor-enabled wheeled lifting robot system, i.e., AMSeWL-R, to facilitate remote autonomous operations. Specifically, AMSeWL-R integrates real-time simultaneous localization and mapping with object detection on a wheeled lifting robot. Additionally, a novel mobile-ROS interaction method is proposed to achieve real-time communication and control between a mobile device and a ROS host. Furthermore, a lightweight object detection algorithm based on YOLOv8, i.e., YOLOv8-R, is proposed to achieve faster detection. Experimental results validate the effectiveness of the AMSeWL-R system for accurately detecting objects and mapping its surroundings. Furthermore, TensorRT acceleration is employed during practical testing on a Jetson Nano to achieve real-time detection using the proposed YOLOv8-R, demonstrating its efficacy in real-world scenarios.

Keywords

• simultaneous localization and mapping
• object detection
• wheeled lifting robot
• mobile-ROS interaction
• real-world tests