# Nonlinear Stochastic Position and Attitude Filter on the Special   Euclidean Group 3

**Authors:** Hashim A. Hashim, Lyndon J. Brown, Kenneth McIsaac

arXiv: 1812.00993 · 2019-04-30

## TL;DR

This paper develops a nonlinear stochastic filter directly on SE(3) for pose estimation, ensuring bounded errors and convergence despite high noise, demonstrated through simulations showing robustness and effectiveness.

## Contribution

It introduces a novel stochastic pose filter on SE(3) with proven stability and convergence properties, handling high noise and bias in measurements.

## Key findings

- Errors are semi-globally uniformly ultimately bounded in mean square.
- Errors converge to a small neighborhood of the origin in probability.
- Simulation results confirm robustness against high noise and bias.

## Abstract

This paper formulates the pose estimation problem as nonlinear stochastic filter kinematics evolved directly on the Special Euclidean Group SE(3). Proposed filter guarantees that the errors present in position and Rodriguez vector estimates are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and that they converge to small neighborhood of the origin in probability. Simulation results show the robustness and effectiveness of the proposed filter in presence of high levels of noise and bias associated with the velocity vector as well as body-frame measurements. Keywords: Pose estimator, pose observer, attitude estimate, control, estimator, observer, Nonlinear stochastic pose filter, stochastic differential equations, Brownian motion process, Ito, Stratonovich, Wong Zakai, unit-quaternion, special orthogonal group, homogeneous transformation matrix, complimentary filter, Euler angles, Angle-axis, mapping, Parameterization, Representation, Robust, Multiplicative Extended Kalman Filter, Unscented Kalman Filter, Particle filter, KF, EKF, IEKF, UKF, MEKF, partial derivative, small, dynamics, equilibrium, asymptotic, covariance, expected value, zero, unknown, time-varying, global, semi-global, stable, stability, uncertain, Gaussian, colored, white, noise, vectorial measurement, vector measurement, translational velocity, angular velocity, singular value decomposition, rotational matrix, identity, deterministic, comparison, inertial frame, rigid body, three dimensional, 3D, space, adjoint, Lie group, projection, landmark, feature, Gyroscope, micro electromechanical systems, Inertial measurement units, sensor, IMUs, Fixed, moving, orientation, Roll, Pitch, Yaw, SVD, UAVs, QUAV, unmanned, underwater vehicle, robot, Robotic System, Spacecraft, quadrotor, quadcopter, integral, advantage, disadvantage, Comparative study, Review, Overview, Survey, autonomous, xyz, axis, SO(3), SE(3).

## Full text

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## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/1812.00993/full.md

## References

35 references — full list in the complete paper: https://tomesphere.com/paper/1812.00993/full.md

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Source: https://tomesphere.com/paper/1812.00993