Pedestrian navigation systems (PNS) using foot-mounted MEMS inertial sensors use zero-velocity updates (ZUPTs) to reduce drift in navigation solutions and estimate inertial sensor errors. However, it is well known that ZUPTs cannot reduce all errors, especially as heading error is not observable. Hence, the position estimates tend to drift and even cyclic ZUPTs are applied in updated steps of the Extended Kalman Filter (EKF). This urges the use of other motion constraints for pedestrian gait and any other valuable heading reduction information that is available. In this paper, we exploit two more motion constraints scenarios of pedestrian gait: (1) walking along straight paths; (2) standing still for a long time. It is observed that these motion constraints (called “virtual sensor”), though considerably reducing drift in PNS, still need an absolute heading reference. One common absolute heading estimation sensor is the magnetometer, which senses the Earth’s magnetic field and, hence, the true heading angle can be calculated. However, magnetometers are susceptible to magnetic distortions, especially in indoor environments. In this work, an algorithm, called magnetic anomaly detection (MAD) and compensation is designed by incorporating only healthy magnetometer data in the EKF updating step, to reduce drift in zero-velocity updated INS. Experiments are conducted in GPS-denied and magnetically distorted environments to validate the proposed algorithms.

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This paper provides algorithms to estimate absolute navigation information, e.g., absolute attitude and position, by using low power, weight and volume Microelectromechanical Systems-type (MEMS) sensors that are suitable for micro planetary rovers. Planetary rovers appear to be easily navigable robots due to their extreme slow speed and rotation but, unfortunately, the sensor suites available for terrestrial robots are not always available for planetary rover navigation. This makes them difficult to navigate in a completely unexplored, harsh and complex environment. Whereas the relative attitude and position can be tracked in a similar way as for ground robots, absolute navigation information, unlike in terrestrial applications, is difficult to obtain for a remote celestial body, such as Mars or the Moon. In this paper, an algorithm called the EASI algorithm (Estimation of Attitude using Sun sensor and Inclinometer) is presented to estimate the absolute attitude using a MEMS-type sun sensor and inclinometer, only. Moreover, the output of the EASI algorithm is fused with MEMS gyros to produce more accurate and reliable attitude estimates. An absolute position estimation algorithm has also been presented based on these on-board sensors. Experimental results demonstrate the viability of the proposed algorithms and the sensor suite for low-cost and low-weight micro planetary rovers.

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