Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Improved Heuristic Drift Elimination (iHDE) for pedestrian navigation in complex buildings

Abstract: The main problem of Pedestrian Dead-Reckoning (PDR) using only a body-attached IMU is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable. Recently, a new method was proposed called Heuristic Drift Elimination (HDE) that minimizes the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of four possible directions. In this paper we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings. We also propose an improved HDE method called iHDE, that is implemented over a PDR framework that uses foot-mounted inertial navigation with an Extended Kalman Filter (EKF). The EKF is fed with the iHDE-estimated orientation error, as well as the confidence over that correction. We experimentally evaluated the performance of the proposed iHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and iHDE outperforms HDE for non-ideal trajectories (e.g. curved paths).

A Cost-Effective Sideslip Estimation Method Using Velocity Measurements From Two GPS Receivers

Abstract: This paper demonstrates that the vehicle sideslip can be estimated through the kinematic relationship of velocity measurements from two low-cost GPS receivers. To compensate for the low update rate of low-cost GPS receivers, acceleration/angular rate measurements from an inertial measurement unit (IMU) are merged with the GPS measurements using an extended Kalman filter (EKF). Two technical challenges were addressed: 1) unsynchronized updates of the two GPS receivers and 2) significant delays in GPS velocity measurement. A stochastic observability analysis reveals that the proposed method guarantees the observability when a vehicle has nonzero yaw rates. Experimental verification shows that the vehicle sideslip is estimated regardless of surface friction levels under several maneuvers.

Optimization-Based Estimator Design for Vision-Aided Inertial Navigation

Abstract: This paper focuses on the problem of real-time pose tracking using visual and inertial sensors in systems with limited processing power. Our main contribution is a novel approach to the design of estimators for these systems, which optimally utilizes the available resources. Specifically, we design a hybrid estimator that integrates two algorithms with complementary computational characteristics, namely a sliding-window EKF and EKF-SLAM. To decide which algorithm is best suited to process each of the available features at runtime, we learn the distribution of the feature number and of the lengths of the feature tracks. We show that using this information, we can predict the expected computational cost of each feature-allocation policy, and formulate an objective function whose minimization determines the optimal way to process the feature data. Our results demonstrate that the hybrid algorithm outperforms each individual method (EKF-SLAM and sliding-window EKF) by a wide margin, and allows processing the sensor data at real-time speed on the processor of a mobile phone.

Modeling and bounding low cost inertial sensor errors

Abstract: This paper presents a methodology for developing models for the post-calibration residual errors of inexpensive inertial sensors in the class normally referred to as ldquoautomotiverdquo or ldquoconsumerrdquo grade. These sensors are increasingly being used in real-time vehicle navigation and guidance systems. However, manufacturer supplied specification sheets for these sensors seldom provide enough detail to allow constructing the type of error models required for analyzing the performance or assessing the risk associated with navigation and guidance systems. A methodology for generating error models that are accurate and usable in navigation and guidance systemspsila sensor fusion and risk analysis algorithms is developed and validated. Use of the error models is demonstrated by a simulation in which the performance of an automotive navigation and guidance system is analyzed.

Attitude determination method and system

Abstract: A method for use in vehicle attitude determination includes generating GPS attitude solutions for a vehicle using three or more antennas receiving GPS signals from two or more space vehicles. An inertial navigation system is initialized by setting the attitude of the inertial navigation system to a GPS attitude solution generated for the vehicle and/or the attitude of the inertial navigation system is updated using the GPS attitude solutions generated for the vehicle or GPS estimated attitude error generated for the vehicle. A system for use in vehicle navigation is also provided. The system generally includes three or more GPS antenna/receiver sets associated with a vehicle, an inertial measurement unit that provides inertial measurements for the vehicle, a processing unit of the system having the capability for generating GPS attitude computations for the vehicle using the three or more GPS antenna/receiver sets and signals from two or more space vehicles; the GPS attitude computations include at least one of absolute attitudes and estimated attitude errors. The processing unit of the system also includes a filter for generating estimates of attitude for the vehicle using the inertial measurements from the inertial measurement unit and the attitude computations.