3,471 research outputs found
A discrete-time attitude observer on SO(3) for vision and GPS fusion
This paper proposes a discrete-time geometric attitude observer for fusing monocular vision with GPS velocity measurements. The observer takes the relative transformations obtained from processing monocular images with any visual odometry algorithm and fuses them with GPS velocity measurements. The objectives of this sensor fusion are twofold; first to mitigate the inherent drift of the attitude estimates of the visual odometry, and second, to estimate the orientation directly with respect to the North-East-Down frame. A key contribution of the paper is to present a rigorous stability analysis showing that the attitude estimates of the observer converge exponentially to the true attitude and to provide a lower bound for the convergence rate of the observer. Through experimental studies, we demonstrate that the observer effectively compensates for the inherent drift of the pure monocular vision based attitude estimation and is able to recover the North-East-Down orientation even if it is initialized with a very large attitude error.Alireza Khosravian, Tat-Jun Chin, Ian Reid, Robert Mahon
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Navigation in Global Positioning Systems (GPS)-denied environments requires
robust estimators reliant on fusion of inertial sensors able to estimate
rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB)
and Inertial Measurement Unit (IMU) represent low-cost measurement technology
that can be utilized for successful Inertial Navigation. This paper presents a
nonlinear deterministic navigation observer in a continuous form that directly
employs UWB and IMU measurements. The estimator is developed on the extended
Special Euclidean Group and ensures exponential
convergence of the closed loop error signals starting from almost any initial
condition. The discrete version of the proposed observer is tested using a
publicly available real-world dataset of a drone flight. Keywords:
Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system,
GPS-denied navigation.Comment: 2023 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
This paper derives a contact-aided inertial navigation observer for a 3D
bipedal robot using the theory of invariant observer design. Aided inertial
navigation is fundamentally a nonlinear observer design problem; thus, current
solutions are based on approximations of the system dynamics, such as an
Extended Kalman Filter (EKF), which uses a system's Jacobian linearization
along the current best estimate of its trajectory. On the basis of the theory
of invariant observer design by Barrau and Bonnabel, and in particular, the
Invariant EKF (InEKF), we show that the error dynamics of the point
contact-inertial system follows a log-linear autonomous differential equation;
hence, the observable state variables can be rendered convergent with a domain
of attraction that is independent of the system's trajectory. Due to the
log-linear form of the error dynamics, it is not necessary to perform a
nonlinear observability analysis to show that when using an Inertial
Measurement Unit (IMU) and contact sensors, the absolute position of the robot
and a rotation about the gravity vector (yaw) are unobservable. We further
augment the state of the developed InEKF with IMU biases, as the online
estimation of these parameters has a crucial impact on system performance. We
evaluate the convergence of the proposed system with the commonly used
quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our
experimental evaluation using a Cassie-series bipedal robot shows that the
contact-aided InEKF provides better performance in comparison with the
quaternion-based EKF as a result of exploiting symmetries present in the system
dynamics.Comment: Published in the proceedings of Robotics: Science and Systems 201
Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering
This paper investigates the use of depth images as localisation sensors for
3D map building. The localisation information is derived from the 3D data
thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the
ICP, and thus of the localization error, is analysed, and described by a Fisher
Information Matrix. It is advocated this error can be much reduced if the data
is fused with measurements from other motion sensors, or even with prior
knowledge on the motion. The data fusion is performed by a recently introduced
specific extended Kalman filter, the so-called Invariant EKF, and is directly
based on the estimated covariance of the ICP. The resulting filter is very
natural, and is proved to possess strong properties. Experiments with a Kinect
sensor and a three-axis gyroscope prove clear improvement in the accuracy of
the localization, and thus in the accuracy of the built 3D map.Comment: Submitted to IROS 2012. 8 page
Observer-based Controller for VTOL-UAVs Tracking using Direct Vision-Aided Inertial Navigation Measurements
This paper proposes a novel observer-based controller for Vertical Take-Off
and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive
measurements from a Vision-Aided Inertial Navigation System (VA-INS) and
produce the required thrust and rotational torque inputs. The VA-INS is
composed of a vision unit (monocular or stereo camera) and a typical low-cost
6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a
gyroscope. A major benefit of this approach is its applicability for
environments where the Global Positioning System (GPS) is inaccessible. The
proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately
estimate attitude (orientation), gyroscope bias, position, and linear velocity.
Ability to use VA-INS measurements directly makes the proposed observer design
more computationally efficient as it obviates the need for attitude and
position reconstruction. Once the motion components are estimated, the
observer-based controller is used to control the VTOL-UAV attitude, angular
velocity, position, and linear velocity guiding the vehicle along the desired
trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and
the control errors of the observer-based controller are proven to be
exponentially stable starting from almost any initial condition. To achieve
global and unique VTOL-UAV representation in 6 DoF, the proposed approach is
posed on the Lie Group and the design in unit-quaternion is presented. Although
the proposed approach is described in a continuous form, the discrete version
is provided and tested. Keywords: Vision-aided inertial navigation system,
unmanned aerial vehicle, vertical take-off and landing, stochastic, noise,
Robotics, control systems, air mobility, observer-based controller algorithm,
landmark measurement, exponential stability
The Invariant Unscented Kalman Filter
International audienceThis article proposes a novel approach for nonlinear state estimation. It combines both invariant observers theory and unscented filtering principles whitout requiring any compatibility condition such as proposed in the -IUKF algorithm. The resulting algorithm, named IUKF (Invariant Unscented Kalman Filter), relies on a geometrical-based constructive method for designing filters dedicated to nonlinear state estimation problems while preserving the physical invariances and systems symmetries. Within an invariant framework, this algorithm suggests a systematic approach to determine all the symmetry- preserving terms without requiring any linearization and highlighting remarkable invariant properties. As a result, the estimated covariance matrices of the IUKF converge to quasi-constant values due to the symmetry-preserving property provided by the invariant framework. This result enables the development of less conservative robust control strategies. The designed IUKF method has been successfully applied to some relevant practical problems such as the estimation of attitude for aerial vehicles using low-cost sensors reference systems. Typical experimental results using a Parrot quadrotor are provided in this pape
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
A hybrid observer for localization from noisy inertial data and sporadic position measurements
We propose an asymptotic position and speed observer for inertial navigation in the case where the position measurements are sporadic and affected by noise. We cast the problem in a hybrid dynamics framework where the continuous motion is affected by unknown continuous-time disturbances and the sporadic position measurements are affected by discrete-time noise. We show that the peculiar hybrid cascaded structure describing the estimation error dynamics is globally finite-gain exponentially ISS with gains depending intuitively on our tuning parameters. Experimental results, as well as the comparison with an Extended Kalman Filter (EKF), confirm the effectiveness of the proposed solution with an execution time two orders of magnitude faster and with a simplified observer tuning because our bounds are an explicit function of the observer tuning knob
Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation
This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements
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