Accurate and Efficient Self-Localization of AGV Relying on Trusted Area Information in Dynamic Industrial Scene

Abstract

Automatic Guided Vehicles (AGVs) are crucial for improving the operational efficiency of industrial scenes, however the self-localization of AGVs faces severe challenges in a dynamic environment due to a large number of observation noises. To address the above issue, this paper presents a trusted area information based self-localization method to achieve efficient and accurate localization in a highly dynamic scene, where the proportion of dynamic obstacles is close to 50%. First of all, the “coarse-noise filter-fine” self-localization framework is proposed to establish the foundation of accurate and efficient localization. Then, the morphology-based trusted area selection is adopted to efficaciously extract the trusted area, in which the probability of appearing dynamic objects is low. After that, based on dynamic angular resolution filtering considering the trusted area, a robust point cloud generation is designed to generate trusted area information. Trusted area information contains dense reliable point cloud in the statice area and spares observation noise in the dynamic area, thus improving the performance of observation models and scan matching. Finally, the self-developed AGV is adopted to verify the effectiveness of the proposed self-localization system in real and simulated dynamic scenes.