Active range sensing for mobile robot localization

Arsénio, Artur; Ribeiro, M. Isabel

doi:10.1109/iros.1998.727440

Cited by 28 publications

(16 citation statements)

References 8 publications

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“…However, there is some work done in the field of active localisation, mainly in land robotics. [2] proposed an active choice of the landmark to be observed. However, this approach does not solve the global localisation problem, but helps tracking the position of the robot.…”

Section: Related Workmentioning

confidence: 99%

Particle diversity reduction for AUV's active localisation

Maurelli

Pétillot

2010

Oceans'10 Ieee Sydney

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An Autonomous Underwater Vehicle (AUV) needs to demonstrate a number of capabilities, in order to carry on autonomous missions with success. One of the key areas is autonomous localisation, i.e. the capability of the AUV to estimate correctly its position and orientation in the environment. However, most of the proposed approaches are "passive", with no robot motion control involved. The "active" localisation incorporates the control of the robot motion, as a way to improve the robustness of the AUV localisation, finding the best path to follow in order to reduce the uncertainty in the state estimation. Representing the vehicle's belief of the state with particles, the active localisation module is triggered when there is a clear grouping in the particles and it produces as an output the path to be followed in order to reduce the uncertainty. Both simulation results and tank trials have shown the advantages of using this technique compared to the classical ones.

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Section: Related Workmentioning

confidence: 99%

Particle diversity reduction for AUV's active localisation

Maurelli

Pétillot

2010

Oceans'10 Ieee Sydney

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“…However, as discussed in Section 4.1.2, the sequence of measurements integration may lead the sequential Kalman filtering approaches to diverge. We have noticed in the literature different policies, using as criterion the uncertainty of the measurements [3], the trace of the a posteriori covariance matrix Λx k [4], or the Mahalanobis distance [58]. In our point of view, it is not easy to choose a given policy.…”

Section: Remarks About Kalman Filtering For Robot Localizationmentioning

confidence: 99%

Robustified estimation algorithms for mobile robot localization based on geometrical environment maps

Borges¹,

Aldon²

2003

Robotics and Autonomous Systems

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“…To estimate the state of the vehicle, there are two types of sensors, that is internal sensor and external sensor. As a method of using the external sensor, for example, state of the vehicle is estimated by the estimated self-position in Laser Range Finder [6] [7]. In this method, it is possible to highly accurate state estimation, however, need the maps information or landmark.…”

Section: Introductionmentioning

confidence: 99%

Velocity estimation using EKF for caster odometers — Numerical verification

Yonezawa

Sekiguchi

Nonaka

2015

2015 IEEE International Conference on Mechatronics (ICM)

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In this paper, velocity estimation method for caster odometers is proposed. Caster odometer is a sensor of travel distance and attitude angle using caster mechanism that has twodegree of rotational freedoms. We calculate velocities of a vehicle utilizing the output measured by caster odometers , however, the caster mechanism has nonlinearity component. To deal with the nonlinearity of the caster mechanism, a velocity is estimated by the extended Kalman filter. It is expected to reduce the effect of noise with higher precision than conventional methods (approximated differentiation, extended Luenberger observer). We confirm the accuracy of the proposed estimation by comparing to the conventional method in numerical simulation.

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Active range sensing for mobile robot localization

Cited by 28 publications

References 8 publications

Particle diversity reduction for AUV's active localisation

Particle diversity reduction for AUV's active localisation

Robustified estimation algorithms for mobile robot localization based on geometrical environment maps

Velocity estimation using EKF for caster odometers — Numerical verification

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Active range sensing for mobile robot localization

Cited by 28 publications

References 8 publications

Particle diversity reduction for AUV's active localisation

Particle diversity reduction for AUV's active localisation

Robustified estimation algorithms for mobile robot localization based on geometrical environment maps

Velocity estimation using EKF for caster odometers &#x2014; Numerical verification

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Velocity estimation using EKF for caster odometers — Numerical verification