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.

Strapdown Inertial Navigation Integration Algorithm Design Part 2 : Velocity and Position Algorithms

Abstract: This series of two papers (Parts 1 and 2) provides a rigorous comprehensive approach to the design of the principal software algorithmsutilized in modern-day strapdown inertial navigation systems: integration of angular rate into attitude, acceleration transformation/integration into velocity, and integration of velocity into position. The algorithmsare structured utilizing the two-speed updatingapproachoriginallydeveloped for attitudeupdating;an analyticallyexact equation is used at moderate speed to update the integration parameter (attitude, velocity, or position)with input provided from a high-speed algorithmmeasuring rectiŽ ed dynamicmotionwithin the parameter update time interval [coning for attitude updating, sculling for velocity updating, and scrolling (writer’s terminology) for high-resolutionpositionupdating].The algorithmdesign approachaccounts for angularrate/speciŽ c force acceleration inputs from the strapdown system inertial sensors, as well as rotation of the navigation frame used for attitude referencing and velocity integration. The Part 1 paper (Savage, P. G., “Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms,” Journal of Guidance, Control, and Dynamics, Vol. 21, No. 1, 1998, pp. 19–28) deŽ ned the overall design requirement for the strapdown inertial navigation integration function and developed the attitude updating algorithms. This paper, Part 2, deals with design of the acceleration transformation/velocity integration and position integration algorithms. Although Parts 1 and 2 often cover basic concepts, the material presented is intended for use by the practitioner who is already familiar with inertial navigation fundamentals.

A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

Abstract: Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (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 extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation 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 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

A new multi-position calibration method for MEMS inertial navigation systems

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

A Practical Visual Servo Control for an Unmanned Aerial Vehicle

Abstract: An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data, along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller, that ensures exponential convergence of the system considered, is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV, developed by the French Atomic Energy Commission, demonstrate the robustness and performance of the proposed control strategy.