Inertial reference unit
About: Inertial reference unit is a(n) research topic. Over the lifetime, 1306 publication(s) have been published within this topic receiving 22068 citation(s). The topic is also known as: IRU.
Papers published on a yearly basis
••01 Jun 1995
TL;DR: A low-cost solid-state inertial navigation system for mobile robotics applications is described and error models for the inertial sensors are generated and included in an extended Kalman filter for estimating the position and orientation of a moving robot vehicle.
Abstract: A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an extended Kalman filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two different solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. Similar error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. >
24 Nov 2000
TL;DR: In this article, the global positioning system (GPS) geodetic application is considered and an initialization and alignment of the GPS system is described in terms of the inertial measurement unit (IMU).
Abstract: Coordinate frames and transformations ordinary differential equations inertial measurement unit inertial navigation system system error dynamics stochastic processes and error models linear estimation INS initialization and alignment the global positioning system (GPS) geodetic application.
TL;DR: This paper describes an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU), which demonstrates accurate estimation of both the calibration parameters and the local scene structure.
Abstract: Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the cameraâIMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.
•18 Feb 1993
TL;DR: In this article, the first and second position estimates are combined and filtered using novel techniques to derive a more accurate third position estimate of the vehicle's position, which can be used for autonomous navigation.
Abstract: Systems and methods allow for the accurate determination of the terrestrial position of an autonomous vehicle in real time. A first position estimate of the vehicle 102 is derived from satellites of a global positioning system and/or a pseudolite(s). The pseudolite(s) may be used exclusively when the satellites are not in the view of the vehicle. A second position estimate is derived from an inertial reference unit and/or a vehicle odometer. The first and second position estimates are combined and filtered using novel techniques to derive a more accurate third position estimate of the vehicle's position. Accordingly, accurate autonomous navigation of the vehicle can be effectuated using the third position estimate.
TL;DR: A new multi-position calibration method was designed for MEMS of high to medium quality that has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes.
Abstract: The Global Positioning System (GPS) is a worldwide navigation system that requires a clear line of sight to the orbiting satellites For land vehicle navigation, a clear line of sight cannot be maintained all the time as the vehicle can travel through tunnels, under bridges, forest canopies or within urban canyons In such situations, the augmentation of GPS with other systems is necessary for continuous navigation Inertial sensors can determine the motion of a body with respect to an inertial frame of reference Traditionally, inertial systems are bulky, expensive and controlled by government regulations Micro-electro mechanical systems (MEMS) inertial sensors are compact, small, inexpensive and most importantly, not controlled by governmental agencies due to their large error characteristics Consequently, these sensors are the perfect candidate for integrated civilian navigation applications with GPS However, these sensors need to be calibrated to remove the major part of the deterministic sensor errors before they can be used to accurately and reliably bridge GPS signal gaps A new multi-position calibration method was designed for MEMS of high to medium quality The method does not require special aligned mounting and has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes A turntable was used to provide a strong rotation rate signal as reference for the estimation of these errors Two different quality MEMS IMUs were tested in the study The calibration results were first compared directly to those from traditional calibration methods, eg six-position and rate test Then the calibrated parameters were applied in three datasets of GPS/INS field tests to evaluate their accuracy indirectly by comparing the position drifts during short-term GPS signal outages
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