Benchmarking for the indoor localization of autonomous mobile robots in intralogistics
This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.
620: Engineering and Applied Operations
380: Commerce, Communications, Transport