Enabling Robust Localization for Automated Guided Carts in Dynamic Environments

Abstract

The range of applications for autonomous guided carts (AGC) is increasingly growing. Especially in industrial environments ensuring high safety standards in combination with high availability and flexibility are major requirements. For this reason, knowledge about its own position in the environments becomes particularly important. For AGC with low vehicle height localization approaches based on contour observations are widespread. However, in over-time-changing environments the robustness of these techniques is limited. This paper proposes an approach for updating the underlying map in real time during operation. This map update allows for a long-term robust localization. The proposed approach is evaluated for a dynamic test scenario using a cellular transport vehicle.