Compensated Heading Angles for Outdoor Mobile Robots in Magnetically Disturbed Environment

Abstract

Heading information is critically important for autonomous mobile robots as it is necessary for scanning or sweeping predetermined areas for specific tasks. Fusing sensor data including angular rates, acceleration, and geomagnetic fields provide heading and attitude. However, the geomagnetic field is often interfered with by ferromagnetic objects or other magnetic sources, resulting in incorrect heading information. This paper describes an algorithm that detects and rejects magnetic disturbances contained in a geomagnetic field. This algorithm combined with an extended Kalman filter is implemented in a relatively low-cost, small-scale microprocessor and sensor module. The algorithm is detailed for parameters that detect magnetic disturbances. The algorithm is also evaluated outdoors by driving a mobile robot on a lawn with apparent ferromagnetic objects and on the flat roof of a ferroconcrete building that includes iron bars and electrical wires in or under the roof. The experimental results on a flat roof indicate that the algorithm improves the accuracy of the heading significantly by reducing the peak-to-peak error by 32.9% (or the rms error by 69.9%).