Improving Tightly LiDAR/Compass/Encoder-Integrated Mobile Robot Localization with Uncertain Sampling Period Utilizing EF
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Improving Tightly LiDAR/Compass/Encoder-Integrated Mobile Robot Localization with Uncertain Sampling Period Utilizing EFIR Filter Yuan Xu1
· Yuriy S. Shmaliy2 · Wanfeng Ma1 · Xianwei Jiang3 · Tao Shen1 · Shuhui Bi1 · Hang Guo4
Accepted: 21 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the uncertain sampling period for the tightly-fused light detection and ranging (LiDAR), compass, and encoder data. The errors of position and velocity, the robot’s yaw, and the sampling period are chosen as state variables. The ranges between the corner feature points (CFPs) and the mobile robot measured by the LiDAR, compass, and encoder are considered as an observation. Based on the tightly-integrated nonlinear model, the extended unbiased finite-impulse response (EFIR) filter fuses the sensors’ data for the integrated localization system. The performances of the traditional loosely-coupled integration scheme, tightly-coupled integration scheme with a constant sampling interval, and tightly-coupled integration with an uncertain sampling interval are compared based on real data. It is shown experimentally that the proposed scheme is more accurate then the traditional loosely-coupled integration and the one relying on a constant sampling interval, which improves by about 10.2%. Keywords Tightly integration · Extended unbiased finite-impulse response (EFIR) · Uncertain sampling period
1 Introduction Since mobile robotics has found wide applications, accurate robot navigation information in Global Positioning System (GPS)-denied and indoor environments has become a research hotspot [1, 14]. Accordingly, many indoor localization technologies using short-range wireless communication have been proposed. For example, in [22], a radio frequency identification (RFID) reader-fault-adaptive localization algorithm has been designed to obtain an acceptable
This work by Y. Xu was supported in part by Science and Technology Project of Universities in Shandong Province (Grant J18KA333), in part by Shandong Provincial Natural Science Foundation (Grant ZR2018PF009, ZR2018LF010). The work by Y. S. Shmaliy was partly supported by the Mexican CONACyTSEP Project A1-S-10287, Funding CB2017-2018. Shuhui Bi
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Extended author information available on the last page of the article.
localization accuracy when some readers malfunction. One custom RFID-embedded smart e-health platform is proposed in [9] for the tagged people in indoor environments. The bluetooth-based localization system has been proposed in [10] to improve the localization accuracy. A robust WiFi localization system has been designed in [5] by fusing derivative fingerprints of received signal strength (RSS) with multiple classifiers (DIFMIC). Note that the localization accuracies of the RFID- and bluetooth-based localization technologies are at meter level, which is n
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