Nonlinear Stochastic Estimators on the Special Euclidean Group SE(3) Using Uncertain IMU and Vision Measurements

Hashim, Hashim A.; Lewis, Frank L.

doi:10.1109/tsmc.2020.2980184

Cited by 30 publications

(44 citation statements)

References 33 publications

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“…where P ∈ R 3 refers to position and R ∈ SO (3) refers to orientation, commonly termed attitude, which together constitute a homogeneous transformation matrix [20], [22] T = Z(R, P ) =…”

Section: Preliminaries Of Slam N (3)mentioning

confidence: 99%

Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias

Hashim

Eltoukhy

2022

IEEE Trans. Intell. Transport. Syst.

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Navigation solutions suitable for cases when both autonomous robot's pose (i.e., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observer for SLAM posed on the manifold of the Lie group of SLAMn (3), characterized by systematic convergence, and designed to mimic the nonlinear motion dynamics of the true SLAM problem. The system error is constrained to start within a known large set and decay systematically to settle within a known small set. The proposed estimator is guaranteed to achieve predefined transient and steady-state performance and eliminate the unknown bias inevitably present in velocity measurements by directly using measurements of angular and translational velocity, landmarks, and information collected by an inertial measurement unit (IMU). Experimental results obtained by testing the proposed solution on a real-world dataset collected by a quadrotor demonstrate the observer's ability to estimate the six-degrees-of-freedom (6 DoF) robot pose and to position unknown landmarks in threedimensional (3D) space.

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Section: Preliminaries Of Slam N (3)mentioning

confidence: 99%

Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias

Hashim

Eltoukhy

2022

IEEE Trans. Intell. Transport. Syst.

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“…In (2), Ω r is defined as overall speed of residual propeller that occurs due to unbalanced rotation of the rotors and is described by the following relationship:…”

Section: Mathematical Model Of Octorotormentioning

confidence: 99%

“…In the past, the quadrotor UAV system is being used extensively in the control community due to the ease in design, low cost, applicability to various control algorithms, and convenience for safe laboratory experiments [1][2][3]. Due to these fringe benefits, the advanced control algorithms such as model predictive control (MPC) in [4], sliding mode control (SMC) technique in [5], backstepping control algorithm in [6], a combination of backstepping and SMC in [7], non-linear dynamic inversion (NDI) controller in [8] and SMC-based NDI technique in [9] are considered for quadrotor system control.…”

Section: Introductionmentioning

confidence: 99%

“…(1) The hybrid AISM-CA-based NDI (AISM-CA/NDI) scheme, proposed in this paper, provides the better tracking performance and contributes towards the additional robustness against the disturbance/uncertainties compared to the conventional NDI controller. (2) In this paper, the fault estimation error is incorporated in the measurement of actuator effectiveness and detailed closed-loop analysis of whole system is performed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive non‐linear integral sliding mode fault‐tolerant control allocation scheme for octorotor UAV system

Ijaz

Chen

Hamayun

2020

IET Control Theory & Appl

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This study proposes a new active fault-tolerant control (FTC) and fault estimation scheme for a non-linear octorotor system. The proposed method utilises the idea of an online control allocation (CA) scheme to fully engage the rotors redundancy based on the information from the fault estimation unit. The nominal performance is first achieved using non-linear dynamic inversion (NDI) technique and then to incorporate the robustness, an adaptive non-linear sliding mode control is united with a baseline NDI controller. The proposed method is used to attain the desired altitude and attitude tracking control of an octorotor system. Furthermore, to control the un-actuated states (called internal dynamics) of octorotor system, a separate integral sliding mode-based NDI controller is designed that provides the translational axes control by generating the desired roll and pitch commands. Simulations on the non-linear model of octorotor system validate the dominant performance of the proposed scheme compared to the existing methods in the literature.

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“…If pose of a robot or vehicle is known, while the map of its surroundings is unknown, the problem is referred to as a mapping problem [1]. On the contrary, if the map of the environment is known, while the pose is unknown, the problem is described as pose estimation [2][3][4][5]. Simultaneous Localization and Mapping (SLAM) combines mapping and pose estimation problems and requires the autonomous system to simultaneously build a map of the environment and track its own pose (i.e.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

Hashim

2021

IEEE Control Syst. Lett.

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A geometric nonlinear observer algorithm for Simultaneous Localization and Mapping (SLAM) developed on the Lie group of SLAMn (3) is proposed. The presented novel solution estimates the vehicle's pose (i.e. attitude and position) with respect to landmarks simultaneously positioning the reference features in the global frame. The proposed estimator on manifold is characterized by predefined measures of transient and steady-state performance. Dynamically reducing boundaries guide the error function of the system to reduce asymptotically to the origin from its starting position within a large given set. The proposed observer has the ability to use the available velocity and feature measurements directly. Also, it compensates for unknown constant bias attached to velocity measurements. Unit-qauternion of the proposed observer is presented. Numerical results reveal effectiveness of the proposed observer.

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Nonlinear Stochastic Estimators on the Special Euclidean Group SE(3) Using Uncertain IMU and Vision Measurements

Cited by 30 publications

References 33 publications

Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias

Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias

Adaptive non‐linear integral sliding mode fault‐tolerant control allocation scheme for octorotor UAV system

Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

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