Auto-localization system for indoor mobile robot using RFID fusion

Abdelgawad, Ahmed

doi:10.1017/s0263574714001106

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…For instance, Chumkamon et al 7 present a blind navigation system using radio frequency identification (RFID) for indoor environments. Abdelgawad 8 employs RFID for indoor localization. An active RFID trilateration and location fingerprinting based on received signal strength indication (RSSI) for pedestrian navigation is proposed by Fu and Guenther.…”

Section: Introductionmentioning

confidence: 99%

Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

Chen

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch-Tung-Striebel smoothing algorithm for people's location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch-Tung-Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch-Tung-Striebel smoothing algorithm is slightly better than the off-line mode.

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Section: Introductionmentioning

confidence: 99%

Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

Chen

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation

Alkhawaja

Jaradat²,

Romdhane³

2023

Robotica

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This paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%.

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