Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots

Abstract

To meet the challenges of making low-cost MEMS yaw rate gyros for the precise self-localization of indoor mobile robots, this paper examines a practical and effective method of minimizing drift on the heading angle that relies solely on integration of rate signals from a gyro. The main idea of the proposed approach is consists of two parts; 1) self-identification of calibration coefficients that affects long-term performance, and 2) threshold filter to reject the broadband noise component that affects short-term performance. Experimental results with the proposed phased method applied to Epson XV3500 gyro demonstrate that it effectively yields minimal drift heading angle measurements getting over major error sources in the MEMS gyro output.

The analysis of Kalman filtering algorithm for land vehicle navigation

“…To minimize the noise of the gyroscope moving average filter was used, which is quite simple to implement [7]. Each filter output value is calculated by averaging the values of the previous series at the input.…”

Indoor vehicle tracking with a smart MEMS sensor

“…The sensor has proved itself useful in a typical navigational task encountered in industrial transport systems. The proposed solution, unlike continuous measurement systems [11], profits from the fact that data received while the vehicle is moving differs significantly from the stationary state and uses it for sensor recalibration (known as zero-velocity update [12]).…”

Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System

“…The gains of the proposed scheme are automatically tuned based on the system dynamic mode (non-acceleration, low-acceleration, and high acceleration mode) sensed by the accelerometers. Hence the system produces robust estimates of vehicle attitude and heading and removes the gyros bias that is a common source of drift error for both dynamic and stationary modes [ 11 , 12 ]. The ADIS16405 MEMS IMU (Inertial Measurement Unit) with magnetic sensors [ 13 ] from Analog Devices Inc. was selected for this research.…”