Majority Rule Sensor Fusion System with Particle Filter for Robust Robot Localization

Ohashi, Norifumi; Funabora, Yuki; Doki, Shinji; Doki, Kae

doi:10.1109/aim.2018.8452415

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…If the distance estimates that have comparatively higher errors (outliers) can be identified and removed, the sensor location estimate will be more accurate. Prior research on methods to detect, eliminate, or suppress such outliers include [24][25][26]. The authors in ref.…”

Section: Ranged-based Schemes Range-free Schemesmentioning

confidence: 99%

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RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

Chuku

Nasipuri

2021

JSAN

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The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and natural habitats, are commonly affected by shadowing due to obstructions caused by natural and man-made obstacles. RF signal attenuation from shadowing introduces uncharacteristically high errors in RSSI-based distance estimates, which result in large errors in RSSI-based localization schemes. This paper proposes the use of outlier detection methods for removing the effect of such disproportionately erroneous distance estimates in location estimation using RSSI. Three different localization schemes are proposed that apply outlier detection to effectively reduce localization errors in shadowed environments. Performance results of the proposed schemes are obtained using computer simulations and experimental tests.

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Section: Ranged-based Schemes Range-free Schemesmentioning

confidence: 99%

“…The authors in ref. [25] used the graph embeddability with rigidity theory to detect and filter outliers in the range estimates. However, they assume that measurements are generally accurate, which is not possible in RSSI-based ranging due to long-term fading effects in terrestrial environments.…”

Section: Ranged-based Schemes Range-free Schemesmentioning

confidence: 99%

RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

Chuku

Nasipuri

2021

JSAN

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“…In order to realize robust localization in various environments, we proposed a sensor fusion system with a relative correlation checking test in [12]. Meanwhile, in [13], we combined our proposed system with a Particle Filter for improving robustness our system to a recursive localization method and confirmed the effectiveness in a limited environment. To demonstrate the robustness of our system in various environments, this paper mentions some results of recursive localization experiments in three environments: the road between buildings, parking spaces, square.…”

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confidence: 92%

“…Localization, estimating a device position based on onboard sensor data, has been researched for vehicles, smartphones, autonomous mobile robots, etc. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, as for autonomous mobile robots, position information is used for map construction, path planning, and object avoidance [21][22][23][24][25][26][27][28][29].…”

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confidence: 99%

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Multisensor robust localization in various environment with correlation checking test

et al. 2021

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Probabilistic localization based on Bayesian theory has been researched as a sensor fusion method to improve the robustness of localization. Pieces of position information, generated by sensors’ observation models with consideration for noises, are fused according to Bayesian theory. However, having large noises not considered in their observation models, the sensors output erroneous position information; thus, the fusion result has a significant error, even when the other sensors output correct ones. In this research, we have proposed a sensor fusion system with a relative correlation checking test to realize robust localization. Pieces of erroneous position information, biased against others and having a negative correlation with others, are detected and excluded in our proposed system by checking their correlation between all of them. The purpose of this paper is to evaluate the robustness of our fusion system by conducting recursive localization experiments in various environments.

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Wireless Sensor Localization Using Outlier Detection

Chuku

Nasipuri

2019

2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT &Amp; IoT and AI (HONET-ICT)

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Majority Rule Sensor Fusion System with Particle Filter for Robust Robot Localization

Cited by 3 publications

References 18 publications

RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

Multisensor robust localization in various environment with correlation checking test

Wireless Sensor Localization Using Outlier Detection

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