The orientation information is critical for the mobile robot. However, the robot’s constrained locomotion, complicated magnetic field environment, and the coupled orientation estimation present challenges for using the microelectromechanical magnetometer in real-time robot orientation determination. This article focuses on calibrating the magnetometer in constrained robot locomotion and achieving the robot’s decoupled roll, pitch, and yaw estimates. A magnitude-free and reference-free calibration approach is proposed to calibrate the magnetometer using the measurements collected from the principal plane of the robot locomotion. Moreover, a decoupled orientation estimator is created under the framework of the extended Kalman filter using the calibrated triaxial magnetic, angular rate, and gravity sensor readings. Two independent experiments validate the effectiveness of magnetometer calibration and orientation decoupling, respectively. A case study of estimating the mobile robot’s orientation shows that the proposed methods can achieve accurate heading estimation and decouple the roll and pitch from the magnetic field data.

Accurate orientation estimation using low-cost inertial and magnetic sensors is important. However, since the magnetometer measurements affect pitch and roll estimates in addition to yaw determination, the resultant accuracy reduction under magnetic disturbances is adverse for special occasions where high-precision pitch and roll estimates are essential. This article presents a double-extended Kalman filter (DEKF) orientation estimator with unit quaternion as output, which decouples magnetometer effects on pitch and roll estimates by decomposing the orientation into tilt quaternion and heading quaternion. The tilt quaternion, including pitch and roll information, is estimated by the first EKF where accelerometer and gyroscope measurements are processed and the inaccurate heading information is discarded. The heading quaternion, solely describing the yaw orientation, is determined by the second EKF, which fuses the vertical component of angular rate measurements and the horizontal component of magnetic measurements. True quaternion is denoted by the multiplicative of tilt and heading quaternions. The separate property of DEKF limits the influences of magnetic disturbances on yaw estimation, making the pitch and roll angles immune to them. Experiment results show that the proposed method solves the coupled disturbance problem without losing accuracy.

The zero-velocity update (ZUPT) technique plays a key role in foot-mounted inertial navigation system, which can minimize the drift errors of low-cost inertial measurement unit (IMU) by resetting the velocity to zero when the foot is stationary. However, it is difficult to detect the zero-velocity interval (ZVI) in mixed locomotion patterns. This article presents a novel ZVI detector based on adaptive simulated energy consumption (SEC) curve. It can identify the ZVIs of various locomotion patterns, such as normal walking, fast walking, running, going upstairs, and downstairs. The SEC curve is generated based on the signal processing approach using the interlaced peak property of the IMU-measured angular rate and acceleration waveforms. Another benefit of the proposed method is that the IMU can be mounted on the instep without restrictions of orientation and position, which improves flexibility and convenience. Experiment results show that the proposed algorithm performs better than three other widely used methods in mixed gait patterns.

This paper focuses on the use of low-cost MEMS (micro-electromechanical systems) MARG (magnetic, angular rate, and gravity) sensors in the field of estimating the joint angles of revolute serial manipulators. In particular, an easy-to-install joint angle estimation method, named the incremental decomposition method (IDM), is proposed. It allows the MEMS MARG sensors to be mounted in any attitude on any position of the manipulator's links, with at least one AMU on the output side of each joint. The IDM is completely independent from the encoder-based joint angle estimator. Hence, it can work as an assistant fault-detection criterion for the economical robotic arms. A robotic arm with six degrees of freedom (DOF) is used as the experimental setup. The IDM can estimate the manipulator's joint angles by fusing the attitude quaternion output by the MARG sensors. To validate the performances and limitations of this method, five experiments have been accomplished in this paper. The experimental results are reported in order, in comparison with the high-resolution encoders.