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Fusing range measurements from ultrasonic beacons and a laser range finder for localization of a mobile robot.

Ko NY, Kuc TY - Sensors (Basel) (2015)

Bottom Line: The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given.The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons.Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics Engineering, Chosun University, 375 Seosuk-dong Dong-gu, Gwangju 501-759, Korea. nyko@chosun.ac.kr.

ABSTRACT
This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because finding the Jacobian matrix is not feasible for range measurement using an LRF, UKF has an advantage in this situation over the extended KF. The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given. Therefore, the proposed method also deals with the problem of data association to determine which beacon corresponds to the given range data. The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons. Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement. In addition, proper adjustment of the UKF design parameters is crucial for full utilization of the UKF approach for sensor fusion. This study contributes to the derivation of a UKF-based design methodology to fuse two exteroceptive measurements that are complementary to each other in localization.

No MeSH data available.


Related in: MedlinePlus

Estimated robot trajectory for the three cases. (a) Estimated trajectory for Case 1; (b) estimated trajectory for Case 2; (c) estimated trajectory for Case 3.
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f3-sensors-15-11050: Estimated robot trajectory for the three cases. (a) Estimated trajectory for Case 1; (b) estimated trajectory for Case 2; (c) estimated trajectory for Case 3.

Mentions: Several tuning parameters are available for the implementation of the UKF. They are αi, i = 1, ⋯, 4 for error covariance M(t) of the proprioceptive measurement noise nu(t) and error covariance Q(t) of the exteroceptive measurement noise n(t). Table 3 lists three different sets of tuning parameter values used to investigate the behavior of the result. Figure 3 shows the estimated trajectories for these cases, and Figure 4 shows the distance error of the estimated trajectories. In Figure 3, five error covariance ellipses are observed at the locations indicated by the blue dots at the center of the ellipses. To make the ellipses more clearly distinguishable, they are magnified 300 times. Table 4 lists the statistical evaluation of the distance error of the location estimation. It shows the mean, standard deviation, root mean square and maximum distance error for the three cases. Table 5 lists the rate of successful association of the USAT range data to the beacons.


Fusing range measurements from ultrasonic beacons and a laser range finder for localization of a mobile robot.

Ko NY, Kuc TY - Sensors (Basel) (2015)

Estimated robot trajectory for the three cases. (a) Estimated trajectory for Case 1; (b) estimated trajectory for Case 2; (c) estimated trajectory for Case 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481944&req=5

f3-sensors-15-11050: Estimated robot trajectory for the three cases. (a) Estimated trajectory for Case 1; (b) estimated trajectory for Case 2; (c) estimated trajectory for Case 3.
Mentions: Several tuning parameters are available for the implementation of the UKF. They are αi, i = 1, ⋯, 4 for error covariance M(t) of the proprioceptive measurement noise nu(t) and error covariance Q(t) of the exteroceptive measurement noise n(t). Table 3 lists three different sets of tuning parameter values used to investigate the behavior of the result. Figure 3 shows the estimated trajectories for these cases, and Figure 4 shows the distance error of the estimated trajectories. In Figure 3, five error covariance ellipses are observed at the locations indicated by the blue dots at the center of the ellipses. To make the ellipses more clearly distinguishable, they are magnified 300 times. Table 4 lists the statistical evaluation of the distance error of the location estimation. It shows the mean, standard deviation, root mean square and maximum distance error for the three cases. Table 5 lists the rate of successful association of the USAT range data to the beacons.

Bottom Line: The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given.The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons.Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics Engineering, Chosun University, 375 Seosuk-dong Dong-gu, Gwangju 501-759, Korea. nyko@chosun.ac.kr.

ABSTRACT
This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because finding the Jacobian matrix is not feasible for range measurement using an LRF, UKF has an advantage in this situation over the extended KF. The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given. Therefore, the proposed method also deals with the problem of data association to determine which beacon corresponds to the given range data. The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons. Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement. In addition, proper adjustment of the UKF design parameters is crucial for full utilization of the UKF approach for sensor fusion. This study contributes to the derivation of a UKF-based design methodology to fuse two exteroceptive measurements that are complementary to each other in localization.

No MeSH data available.


Related in: MedlinePlus