Open Access
Guaranteed mobile robot tracking using interval analysis
Michel Kieffer,Luc Jaulin,Eric Walter,Dominique Meizel +3 more
- 24 Feb 1999
pp 347-360
TL;DR: In this article, a nonlinear state estimator based on interval analysis and the notion of set inversion is applied to robot localization and tracking in the presence of bounded process and measurement noise.
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Abstract: The problem considered here is state estimation in the presence of bounded process and measurement noise. A new nonlinear state estimator, based on interval analysis and the notion of set inversion, is applied to robot localization and tracking. This estimator evaluates a set guaranteed to contain all values of the state that are consistent with the available observations, given the noise bounds and some possibility very large set containing the initial value of the state. Three situations are considered to illustrate the properties of the estimator.
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Citations
•Book
Applied Interval Analysis
Luc Jaulin,Michel Kieffer,Olivier Didrit,Eric Walter +3 more
- 30 Aug 2001
1.8K
Initial localization by set inversion
Dominique Meizel,O. Leveque,Luc Jaulin,E. Walter +3 more
- 01 Dec 2002
TL;DR: Initial localization problems are solved by using set-membership estimation, which can be used with any robot and any kind of sensor(s), provided that a computable model of the environment/sensor interaction is available.
Sensor based robot localisation and navigation: using interval analysis and unscented Kalman filter
I. Ashokaraj,Antonios Tsourdos,Peter Silson,Brian White +3 more
- 20 Jul 2004
TL;DR: This paper describes a sensor based navigation approach using an interval analysis (IA) based adaptive mechanism for an unscented Kalman filter (UKF) that is equipped with inertial sensors, encoders and ultrasonic sensors.
Interval analysis for guaranteed non-linear parameter and state estimation
Michel Kieffer,Eric Walter +1 more
TL;DR: This paper presents some tools based on interval analysis for guaranteed non-linear parameter and state estimation in a bounded-error context that make it possible to compute outer approximations of the set of all parameter or state vectors that are consistent with the model structure, measurements and noise bounds.
30
Nonlinear State Estimation Using Forward-Backward Propagation of Intervals in an Algorithm
Luc Jaulin,Isabelle Braems,Michel Kieffer,Eric Walter +3 more
- 01 Jan 2001
TL;DR: In this paper, the state vector of a discrete-time system from interval output data is estimated by combining set-inversion with forward-backward propagation of intervals through the model.
References
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John J. Leonard,Hugh Durrant-Whyte +1 more
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TL;DR: An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
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Directed Sonar Sensing for Mobile Robot Navigation
John J. Leonard,Hugh Durrant-Whyte +1 more
- 01 Jan 1992
TL;DR: This paper presents a Sonar Sensor Model for Directed Sensing Strategies, which combines model-Based Localization, Simultaneous Map Building, and Simultaneously Map Building and Localization.
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Recursive state estimation for a set-membership description of uncertainty
Dimitri P. Bertsekas,I. Rhodes +1 more
TL;DR: In this paper, the problem of estimating the state of a linear dynamic system using noise-corrupted observations, when input disturbances and observation errors are unknown except for the fact that they belong to given bounded sets, is considered.
755
World modeling and position estimation for a mobile robot using ultrasonic ranging
James L. Crowley
- 14 May 1989
TL;DR: The author describes a system for dynamically maintaining a description of the limits to free space for a mobile robot using a belt of ultrasonic range sensors and a Kalman filter update equation is developed to permit the correspondence of a line segment to the model to be applied as a correction to estimated position.
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