Publication: Improved indoor localization method for a mobile robot using the fusion of zigbee-based rssi and odometry
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Date
2020-08-01
Authors
Loganathan, Anbalagan
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Abstract
Indoor localization is by definition is the method of acquiring the location of a device or user in an indoor setting. Localizing a mobile node in an indoor setting is comparatively more difficult than localizing a static node because it changes its position over time continuously. In addition, for RSSI-based localization, due to the environmental occlusion and the communication modules limitation such as sudden transceiver failures, or restricted energy and bandwidth, at a certain time instance a node can only obtain two RSSI values when moving. Besides, choosing the right communication module is another challenge to be faced when using the RSSI-based localizing method. There is always a trade-off with cost, efficiency, and convenience. Odometry-based localization, on the other hand, suffers from accumulated errors due to the wheel slippage, sensor drift, and other environmental factors. Thus a new method of fusion of localization result of Zigbee-based RSSI values and the odometry is proposed in this work. Initially, an improved path-loss propagation model is generated by using a curve-fitting graph. Then, a preliminary set of experiments was conducted to investigate the accuracy of each method and to identify the optimal weighting parameters before both localization techniques are fused which will compensate for the deficiencies of individual methods. The RSSI received by the mobile node, Node M is smoothed through the Curve Smoothness Index and pass through a median filter with before the coordinates were calculated and used for the fusion with odometry-based results. The results were recorded by allowing the mobile robot to move through three different trajectories. Both numerical and experimental results revealed that the use of the Curve Smoothness index and median filter on the RSSI values improved the RSSI-based localization significantly. The fusion of both odometry and Zigbee-based localization outperformed their individual component’s results. From the experimental results, it was shown that the proposed methods provided significant improvements for all trajectories considered, which ranges from 30% to 36%.