Single beacon navigation: Observability analysis and filter design

Batista, Pedro; Silvestre, Carlos; Oliveira, Paulo

doi:10.1109/acc.2010.5531613

Cited by 37 publications

(32 citation statements)

References 15 publications

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“…State of the art methods include, for instance, Long Base Line (LBL) systems [3], where an on-board transceiver is used to interrogate a fixed number of transponders which are located at a priori known positions. The calibration cost may be reduced if single beacon/transponder methods are used instead [4]. At the cost of decreasing the accuracy, the calibration step may be even avoided if an inverted LBL [5] or Ultra Short Base Line (USBL) [6] is used.…”

Section: Introductionmentioning

confidence: 99%

Bathymetry-based SLAM with difference of normals point-cloud subsampling and probabilistic ICP registration

Palomer

Ridao

Ribas

et al. 2013

2013 MTS/IEEE OCEANS - Bergen

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This paper describes a probabilistic surface matching method for pose-based bathymetry SLAM using a multibeam sonar profiler. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches. Then, a probabilistic implementation of the ICP is used to deal with the uncertainty of the robot pose as well as the measured points in a two-stage process including pointto-point and point-to-plane metrics. A surface adaptation using octrees and difference of normals is proposed to have ICP-derived methods working in feature-poor or highly unstructured areas typical of bathymetric scenarios. Moreover, a heuristic based on the uncertainties of the surface points is used to improve the basic algorithm, decreasing the ICP complexity to O(n). The performance of the method is demonstrated with real data from a bathymetric survey with Girona 500 AUV.

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Section: Introductionmentioning

confidence: 99%

Bathymetry-based SLAM with difference of normals point-cloud subsampling and probabilistic ICP registration

Palomer

Ridao

Ribas

et al. 2013

2013 MTS/IEEE OCEANS - Bergen

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“…One particular example is proposed in Ref. [93], in which a localization framework is introduced for a rover that is tracked by a single beacon that measures range. Another proposed lander-based system is discussed here [94].…”

Section: Fixed Assets/beacons Localizationmentioning

confidence: 99%

“…Batista et al [93] proposes a localization system in which IMU measurements are combined with range measurements from a single beacon on a lander using a Kalman filter. This system would be appropriate for a rover that does not venture far from its lander.…”

Section: Example Systemsmentioning

confidence: 99%

Surface Navigation

Iles

Winter

Silva

et al. 2016

Contemporary Planetary Robotics

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“…Let d ⊥ be a unit vector orthogonal to the plane defined by the three landmarks. Then, is observable on [ t 0 , t f ], t 0 < t f , in the sense that any initial condition is uniquely determined by the corresponding response { y ( t ), t ∈[ t 0 , t f ]} and system input { u ( t ), t ∈ [ t 0 , t f ]}, if the set of functions

\begin{matrix} ℱ_{3} = {t - t_{0} ​, {(t - t_{0})}^{2} ​, {(t - t_{0})}^{3} ​, {(t - t_{0})}^{4} ​, \\ [u^{[2]} (t, t_{0})] \cdot d^{⊥} ​, (t - t_{0}) [u^{[2]} (t, t_{0})] \cdot d^{⊥} ​, \\ {(t - t_{0})}^{2} [u^{[2]} (t, t_{0})] \cdot d^{⊥}} \end{matrix}

is linearly independent on [ t 0 , t f ]. Proof The proof is essentially a combination of the technical contents of the proof for single range measurements, detailed in , and the proof for the LBL configuration. It is presented in .…”

Section: Three and Two Range Measurementsmentioning

confidence: 99%

Sensor‐Based Long Baseline Navigation: Observability Analysis and Filter Design

Batista¹,

Silvestre²,

Oliveira³

2013

Asian Journal of Control

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This paper presents a novel long baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms and allowing also the consideration of the cases of reduced numbers of readings, in particular when there are only two or three distance measurements. The nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally exponentially stable (GES) and it is shown, in a realistic simulation environment, that the filter achieves similar performance to the extended Kalman filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.

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Single beacon navigation: Observability analysis and filter design

Cited by 37 publications

References 15 publications

Bathymetry-based SLAM with difference of normals point-cloud subsampling and probabilistic ICP registration

Bathymetry-based SLAM with difference of normals point-cloud subsampling and probabilistic ICP registration

Surface Navigation

Sensor‐Based Long Baseline Navigation: Observability Analysis and Filter Design

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