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.

An integrated IMU and UWB sensor based indoor positioning system

Abstract: This paper describes an Indoor Positioning System (IPS) that fuses an Ultra-Wideband (UWB) sensor-based positioning solution with an Inertial Measurement Unit (IMU) sensor-based positioning solution to obtain a robust, yet, optimal positioning performance. Sensor fusion is accomplished via an Extended Kalman Filter (EKF) design which simultaneously estimates the IMU sensors' systematic errors and corrects the positioning errors. Fault detection, identification, and isolation are built into the EKF design to prevent the corrupted UWB sensor measurement data due to obstructions, multi-path and other interferences from degrading the positioning performance. General formulation of an IPS using IMU for both pure inertial and Kalman filter aided modes of operation using UWB sensor data is given in the paper for tracking a six degree-of-freedom (DOF) platform motion. The derivations of an 8-state EKF design are detailed in the paper for a 3 DOF (two translational and one angular motions) platform planar motion, where data from a 9-axis Motion Tracking device, MPU-9250, and four UWB radio sensor devices, DWM1000, are used. A Matlab-based simulation model is developed and built to assess the proposed IPS performance along with their performance sensitivities to platform motion profiles, UWB/inertial sensor errors, and filter update rates. With specific motion profiles, computer simulation results indicate that more than 100% positioning performance improvement over the UWB sensor-based positioning solution along can be obtained through the proposed sensor fusion solution. A laboratory test bed for a 3 DOF motion platform is designed, built, and tested to validate the proposed IPS performance. The IPS performance obtained from actual laboratory tests correlated very well with the simulation results.

Attitude determination utilizing an inertial measurement unit and a plurality of satellite transmitters

Abstract: The invention is a method for obtaining observables for input to a Kalman filter process which determines the attitude (roll, pitch, and heading) of a platform. The invention utilizes an inertial measurement unit (IMU) attached to the platform and an associated processor, a plurality of signal receiving antennas attached to the platform, and a plurality of satellite transmitters. The heading of the platform as determined by the IMU and its associated processor by themselves can be significantly in error. A comparison of the values of an attitude-sensitive function of the ranges from the platform antennas to different groupings of satellite transmitters obtained first by using IMU data and second by using the measured phases of the satellite-transmitter signals received at the platform antennas, a very accurate value for the range function is obtained. This accurate value of the range function is used in a Kalman filter process to obtain very accurate values for platform attitude.

Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments

Abstract: Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.

A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

Abstract: This paper presents a novel 3D indoor Laser-aided Inertial Navigation System (L-INS) for the visually impaired. An Extended Kalman Filter (EKF) fuses information from an Inertial Measurement Unit (IMU) and a 2D laser scanner, to concurrently estimate the six degree-of-freedom (d.o.f.) position and orientation (pose) of the person and a 3D map of the environment. The IMU measurements are integrated to obtain pose estimates, which are subsequently corrected using line-to-plane correspondences between linear segments in the laser-scan data and orthogonal structural planes of the building. Exploiting the orthogonal building planes ensures fast and efficient initialization and estimation of the map features while providing human-interpretable layout of the environment. The L-INS is experimentally validated by a person traversing a multistory building, and the results demonstrate the reliability and accuracy of the proposed method for indoor localization and mapping.