Mobile robot localization via sensor fusion algorithms

Uyulan, Çağlar; Ergüzel, Türker Tekin; Arslan, Ersen

doi:10.1109/intellisys.2017.8324245

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…A systematic geometric treatment of noise and diffusion is taken up in [11] and has been employed for noisy motion planning and control analysis. Alternatively, filter-based methods such as EKF and UKF [19], Hybrid EKF [7] and neural networks [20] have been applied to state estimation of based on odometry measurements. With the geometric structure of the noisy equations of motion [9, 10, 13], use-case specific filters may be constructed for WMR localization based on extensions of existing sensor fusion methodology [21].…”

Section: Introductionmentioning

confidence: 99%

Wheeled Mobile Robots on Rough Terrains as Stochastic Nonholonomic Systems

Gzenda¹,

Chhabra²

2023

Preprint

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<p>In this paper, we investigate the motion of wheeled mobile robots on rough terrains modelled as noisy nonholonomic constraints. Such constraints are the natural extension of ideal nonholonomic constraints when the Stratonovich process is directly introduced in the constraint equations. The resulting stochastic model can capture motion on rough surfaces, random skip/uncertainty in the wheel-ground point of contact, or stochastic motion of the surface. We study a differential robot with ideal noisy and affine noisy constraints, where each case models a certain aspect of motion on rough terrains. We then qualitatively investigate their corresponding stochastic dynamics through Monte-Carlo simulations. The proposed stochastic model for roving rough terrains has the potential to serve as the process model in model-based motion estimators relying on measurements from an interoceptive suite of sensors. The challenge will be dealing with the nonlinear appearance of the noise in the equations of motion. </p>

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Section: Introductionmentioning

confidence: 99%