Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in real-time and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.
Inertial navigation systems have been studied for decades. The classical literature cover primarily navigation applications for aircraft and large vehicles @cite_4 @cite_23 @cite_28 @cite_12 . The development of handheld consumer-grade devices has awakened an interest in pedestrian navigation applications, where the challenges are slightly different from those in the classical approaches. That is, the limited quality of smartphone MEMS sensors and abrupt motions of hand-held devices pose additional challenges which have so far prevented generic inertial navigation solutions for smartphone applications.
@cite_28 @cite_4 @cite_12 @cite_23
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Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in realtime and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup. Inertial navigation is widely used for the guidance of aircraft, missiles ships and land vehicles, as well as in a number of novel applications such as surveying underground pipelines in drilling operations. This book discusses the physical principles of inertial navigation, the associated growth of errors and their compensation. It draws current technological developments, provides an indication of potential future trends and covers a broad range of applications. New chapters on MEMS (microelectromechanical systems) technology and inertial system applications are included. Significant developments and technical trends in the area of navigation systems are reviewed. In particular, the integration of the Global Positioning System (GPS) and Inertial Navigation System (INS) has been an important development in modern navigation. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing integrated navigation systems of accuracy, performance, low cost and all the issues that aid in optimizing their operating efficiency. The integration of GPS and INS has been successfully used in practice during the past decades. However, much of the work has focused on the use of a high accuracy Inertial Measurement Unit (IMU), which is an inertial sensors block without navigation solution output, and hence, this research area is also reviewed in this paper. Modern airborne navigation systems for manned and unmanned platforms usually rely on GPS measurements to constrain the inertial position estimate of the platform. This reliance on GPS can quickly cause the navigational estimates of system to become unreliable when the system is operating in GPS-limited or denied areas. This paper presents a vision-aided inertial navigation system that uses ground features (in this case road intersections) matched to a database to provide position measurements. An image processing algorithm is used to extract the shapes of the road intersections from visual imagery, this shape is then matched to a reference database to provide image to map road intersection correspondences. The correspondence information is fused with the inertial solution in an Extended Kalman Filter to constrain the complete attitude and position inertial navigation solution. The system is developed to operate at non-zero attitude angles removing the level flight limitations of past approaches. Flight test results of the system demonstrate that the system can successfully produce accurate navigation estimates that are comparable to the use of GPS without the same limitations of GPS when operating in a GPS-limited or denied area.