High-accuracy vehicle localization for autonomous warehousing

The research presented in this paper aims to bridge the gap between the latest scientific advances in autonomous vehicle localization and the industrial state of the art in autonomous warehousing. Notwithstanding great scientific progress in the past decades, industrial autonomous warehousing systems still rely on external infrastructure for obtaining their precise location. This approach increases warehouse installation costs and decreases system reliability, as it is sensitive to measurement outliers and the external localization infrastructure can get dirty or damaged. Several approaches, well studied in scientific literature, are capable of determining vehicle position based only on information provided by on board sensors, most commonly wheel encoders and laser scanners. However, scientific results published to date either do not provide sufficient accuracy for industrial applications, or have not been extensively tested in realistic, industrial-like operating conditions. In this paper, we combine several well established algorithms into a high-precision localization pipeline, capable of computing the pose of an autonomous forklift to sub-centimeter precision. The algorithms use only odometry information from wheel encoders and range readings from an on board laser scanner. The effectiveness of the proposed solution is evaluated by an extensive experiment that lasted for several days, and was performed in a realistic industrial-like environment. HighlightsWe describe an algorithm stack for high-precision autonomous vehicle localization.Localization accuracy satisfies industrial requirements of 1.5cm and 0.5°.The proposed methodology has been verified by extensive experiments.Experiments were performed in realistic industrial conditions.