Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

Deep Neural Network Based Inertial Odometry Using Low-Cost Inertial Measurement Units

Abstract: Inertial measurement units (IMUs) have emerged as an essential component in many of today's indoor navigation solutions due to their low cost and ease of use. However, despite many attempts for reducing the error growth of navigation systems based on commercial-grade inertial sensors, there is still no satisfactory solution that produces navigation estimates with long-time stability in widely differing conditions. This paper proposes to break the cycle of continuous integration used in traditional inertial algorithms, formulate it as an optimization problem, and explore the use of deep recurrent neural networks for estimating the displacement of a user over a specified time window. By training the deep neural network using inertial measurements and ground truth displacement data, it is possible to learn both motion characteristics and systematic error drift. As opposed to established context-aided inertial solutions, the proposed method is not dependent on either fixed sensor positions or periodic motion patterns. It can reconstruct accurate trajectories directly from raw inertial measurements, and predict the corresponding uncertainty to show model confidence. Extensive experimental evaluations demonstrate that the neural network produces position estimates with high accuracy for several different attachments, users, sensors, and motion types. As a particular demonstration of its flexibility, our deep inertial solutions can estimate trajectories for non-periodic motion, such as the shopping trolley tracking. Further more, it works in highly dynamic conditions, such as running, remaining extremely challenging for current techniques.

Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials

Abstract: We describe a low-cost wearable system that tracks the location of individuals indoors using commonly available inertial navigation sensors fused with radio frequency identification (RFID) tags placed around the smart environment. While conventional pedestrian dead reckoning (PDR) calculated with an inertial measurement unit (IMU) is susceptible to sensor drift inaccuracies, the proposed wearable prototype fuses the drift-sensitive IMU with a RFID tag reader. Passive RFID tags placed throughout the smart-building then act as fiducial markers that update the physical locations of each user, thereby correcting positional errors and sensor inaccuracy. Experimental measurements taken for a 55 m × 20 m 2D floor space indicate an over 1200% improvement in average error rate of the proposed RFID-fused system over dead reckoning alone.

1|A Kalman filter-based algorithm for IMU-camera calibration

Abstract: Vision-aided Inertial Navigation Systems (V-INS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filter-based algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

Optimum Skewed Redundant Inertial Navigators

Abstract: The problem of proper placement of the inertial sensors to optimize system performance is important to system designers. This is especially true of redundant strapdown systems that employ more sensors than the conventional, mutually orthogonal sets of three. In systems designed for a free fall environment and with no preferred direction, such as for spacecraft attitude reference, the sensor input axes should divide the three-space equally, and can thus be viewed as being normal to the faces of regular polyhedra.' In Earth-bound inertial navigators, the effect of gravity-dependent sensor errors and the generally reduced effect of azimuth errors on navigation accuracy significantly alter the situation. Both effects tend to decrease sensor elevation angles in an optimized system. Formulas are derived and curves are drawn for optimum sensor elevation and azimuth angles vs a 0-sensitive sensor error parameter, and a mission relative azimuth error parameter. Tetrad, pentad, and hexad arrays are analyzed, affording a dramatic improvement in accuracy as well as autonomous fault isolation and/or detection capability.

Tightly Coupled Inertial Navigation System With Signals of Opportunity Aiding

Abstract: A tightly coupled inertial navigation system (INS) aided by ambient signals of opportunity (SOPs) is developed. In this system, a navigating vehicle aids its onboard INS using pseudoranges drawn from terrestrial SOPs with unknown emitter positions and clock biases through an extended Kalman filter-based radio simultaneous localization and mapping (SLAM) framework. The SOP-aided INS uses both global navigation satellite system (GNSS) and SOP pseudoranges during GNSS availability periods and switches to using SOP pseudoranges exclusively during GNSS unavailability periods. This framework is studied through numerical simulations by varying: 1) Quantity of exploited SOPs and 2) quality of SOP-equipped oscillators. It is demonstrated that the SOP-aided INS using a consumer-grade IMU produces smaller estimation uncertainties compared to a traditional tightly coupled GNSS-aided INS using a tactical-grade IMU. In the absence of GNSS signals, over the simulation finite-time horizon, the errors produced by the SOP-aided INS appear to be bounded, while the errors produced by a traditional tightly coupled GNSS-aided INS diverge unboundedly. Moreover, the article presents experimental results demonstrating an unmanned aerial vehicle using terrestrial cellular SOPs to aid its onboard consumer-grade IMU in the absence of GNSS signals. It is demonstrated that the final position error of a traditional tightly coupled GNSS-aided INS after 30 s of GNSS cutoff was 57.30 m, while the final position error of the tightly coupled SOP-aided INS was 9.59 m.