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Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.

Besada-Portas E, Lopez-Orozco JA, Lanillos P, de la Cruz JM - Sensors (Basel) (2012)

Bottom Line: This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems.The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors.Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches.

View Article: PubMed Central - PubMed

Affiliation: Departamento Arquitectura de Computadores y Automatica, Universidad Complutense de Madrid, Madrid, Spain. evabes@dacya.ucm.es

ABSTRACT
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.

No MeSH data available.


Prediction and Measurement Update Steps of the adapted EIFAsyn. (a) Prediction step from t − 1 to t; (b) Update step for ξs,k,t (measurement of sensor s with time stamp k arriving at t).
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f4-sensors-12-02487: Prediction and Measurement Update Steps of the adapted EIFAsyn. (a) Prediction step from t − 1 to t; (b) Update step for ξs,k,t (measurement of sensor s with time stamp k arriving at t).

Mentions: The two main steps of this version of EIFAsyn, prediction (carry out to make the filter estimate the state of the next time step) and measurement update (performed when any measurement is arrived), are presented in Figure 4, where ξs,k,t represents, maintaining the nomenclature used in [30,31], the measurement zs,k taken by sensor s at time k that arrives at the localization module at t. Besides, x̂k/k and Pk/k stand for the mean and covariance of the location estimated with all the measurements that have been assimilated so far, ŷk/k and Yk/k for the information of the estimated location and its covariance, ik and Ik/k for the accumulated sensorial information and accumulated sensorial information covariance of the sensors whose information does not need to be recalculated, and and for the accumulated sensorial information and accumulated sensorial information covariance of the sensors whose information has to be recalculated. Finally, the additional operations that do not appear in the original version of EIFAsyn are presented in red to make them easily identifiable.


Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.

Besada-Portas E, Lopez-Orozco JA, Lanillos P, de la Cruz JM - Sensors (Basel) (2012)

Prediction and Measurement Update Steps of the adapted EIFAsyn. (a) Prediction step from t − 1 to t; (b) Update step for ξs,k,t (measurement of sensor s with time stamp k arriving at t).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376572&req=5

f4-sensors-12-02487: Prediction and Measurement Update Steps of the adapted EIFAsyn. (a) Prediction step from t − 1 to t; (b) Update step for ξs,k,t (measurement of sensor s with time stamp k arriving at t).
Mentions: The two main steps of this version of EIFAsyn, prediction (carry out to make the filter estimate the state of the next time step) and measurement update (performed when any measurement is arrived), are presented in Figure 4, where ξs,k,t represents, maintaining the nomenclature used in [30,31], the measurement zs,k taken by sensor s at time k that arrives at the localization module at t. Besides, x̂k/k and Pk/k stand for the mean and covariance of the location estimated with all the measurements that have been assimilated so far, ŷk/k and Yk/k for the information of the estimated location and its covariance, ik and Ik/k for the accumulated sensorial information and accumulated sensorial information covariance of the sensors whose information does not need to be recalculated, and and for the accumulated sensorial information and accumulated sensorial information covariance of the sensors whose information has to be recalculated. Finally, the additional operations that do not appear in the original version of EIFAsyn are presented in red to make them easily identifiable.

Bottom Line: This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems.The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors.Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches.

View Article: PubMed Central - PubMed

Affiliation: Departamento Arquitectura de Computadores y Automatica, Universidad Complutense de Madrid, Madrid, Spain. evabes@dacya.ucm.es

ABSTRACT
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.

No MeSH data available.