Topic
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.
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TL;DR: The experimental results show that the proposed DMIA algorithm can achieve a rapid and accurate in-motion alignment, and introduces the idea of constructing multiple calculation loops for an IMU to maximize the advantages of SINS.
Abstract: In-motion alignment of strapdown inertial navigation systems (SINS) without any geodetic-frame observations is one of the toughest challenges for autonomous vehicles. Considering the characteristics of SINS, this paper presents a dual-model-based in-motion alignment method for the odometer-aided SINS. Two inertial navigation calculation loops are established for an inertial measurement unit (IMU), one of which executes the in-motion gyrocompass horizontal alignment algorithm to decompose the body velocity measured by odometer to navigation frame and attenuate the disturbance. And the other is the attitude determination-based alignment loop, where the vector observation-based SINS alignment is executed. The contributions of the work presented here are twofold. First, the dual-model initial alignment (DMIA) algorithm for SINS is proposed, which introduces the idea of constructing multiple calculation loops for an IMU to maximize the advantages of SINS. Second, depending on the body-frame speed-aided attitude determination and navigation-frame speed-aided attitude determination, the body-frame velocity observation with disturbance is accurately decomposed to navigation frame with the noise attenuated by gyrocompass horizontal alignment. The experimental results show that the proposed DMIA algorithm can achieve a rapid and accurate in-motion alignment.
49 citations
01 Jan 2014
TL;DR: A 25 g nano-quadrotor equipped with a micro PAL-camera and wireless video transmitter is presented, with which it is claimed this is the lightest quadrotor capable of visual-inertial navigation with off-board processing.
Abstract: We present a 25 g nano-quadrotor equipped with a micro PAL-camera and wireless video transmitter, with which we demonstrate autonomous hovering and figure flying using a visual-inertial SLAM system running on a ground-based laptop. To our knowledge this is the lightest quadrotor capable of visual-inertial navigation with off-board processing. Further we show autonomous flight with external pose-estimation, using both a motion capture system or an RGB-D camera. The hardware is low-cost, robust, easily accessible and has freely available detailed specifications. We release all code in the form of an open-source ROS package to stimulate and facilitate further research into using nano-quadrotors as visual-inertial based autonomous platforms.
49 citations
TL;DR: This article applies the generalized method of wavelet moments on error signals issued from MEMS-based inertial sensors by building and estimating composite stochastic processes for which classical methods cannot be used and demonstrates that the GMWM-based calibration framework enables to estimate complex stochastically models in terms of the resulting navigation accuracy that are relevant for the observed structure of errors.
Abstract: The integration of observations issued from a satellite-based system (GNSS) with an inertial navigation system (INS) is usually performed through a Bayesian filter such as the extended Kalman filter (EKF). The task of designing the navigation EKF is strongly related to the inertial sensor error modeling problem. Accelerometers and gyroscopes may be corrupted by random errors of complex spectral structure. Consequently, identifying correct error-state parameters in the INS/GNSS EKF becomes difficult when several stochastic processes are superposed. In such situations, classical approaches like the Allan variance (AV) or power spectral density (PSD) analysis fail due to the difficulty of separating the error processes in the spectral domain. For this purpose, we propose applying a recently developed estimator based on the generalized method of wavelet moments (GMWM), which was proven to be consistent and asymptotically normally distributed. The GMWM estimator matches theoretical and sample-based wavelet variances (WVs), and can be computed using the method of indirect inference. This article mainly focuses on the implementation aspects related to the GMWM, and its integration within a general navigation filter alibration procedure. Regarding this, we apply the GMWM on error signals issued from MEMS-based inertial sensors by building and estimating composite stochastic processes for which classical methods cannot be used. In a first stage, we validate the resulting models using AV and PSD analyses and then, in a second stage, we study the impact of the resulting stochastic models design in terms of positioning accuracy using an emulated scenario with statically observed error signatures. We demonstrate that the GMWM-based calibration framework enables to estimate complex stochastic models in terms of the resulting navigation accuracy that are relevant for the observed structure of errors.
49 citations
TL;DR: This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum- based detectors and memory-based detector to solve the problem of distinction of (in)activity periods in inertial navigation applications.
Abstract: Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The periodical recomputation of these parameters allows the application to maintain a constant degree of precision. This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum-based detectors and memory-based detectors. A robust statistical comparison is carried out by the use of an accelerometer and angular rate signal synthesizer that mimics the output of accelerometers and gyroscopes when subjects are performing basic activities of daily life. Theoretical results are verified by testing the algorithms over signals gathered using an Inertial Measurement Unit (IMU). Detection accuracy rates of up to 97% are achieved.
49 citations
01 Jul 2002
TL;DR: A tightly packaged, portable and inexpensive waypoint navigation system was developed that allows the operator to specify path waypoints on ortho-rectified aerial photographs and run on a low-cost embedded processor.
Abstract: : Land-based waypoint navigation usually requires accurate position informafion to effectively function in either natural or n%an-n%ade terrain. Most systems solve this problem by using differential GPS and/or high-quality, expensive inertial navigation systems. In an effort to make waypoint navigation available to smaller tactical platforms, a tightly packaged, portable and inexpensive waypoint navigation system was developed. This system was implemented on the Man Portable Robotic System (MPRS) Urban Robot (URBOT)'. The package uses inexpensive sensors and a combination of standard Kalman Filter and waypoint following techniques along with some novel approaches to compensate for the deficiencies of the GPS and gyroscope sensors. The algorithms run on a low-cost embedded processor. A control unit was also developed that allows the operator to specify path waypoints on ortho-rectified aerial photographs.
49 citations