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Robust Localization Based on Regularization With Adaptive Scaling: Model, Algorithm, and Application
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DOI：10.1109/JIOT.2026.3674674.png)
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Abstract
En 中文
In challenging environments, conventional positioning algorithms often exhibit performance degradation due to outlier-contaminated sensor measurements. To address this issue, this article proposes a robust localization method based on regularization with adaptive scaling (RAS). The proposed method introduces the scaling factor to characterize the amplification of noise variance caused by outliers and integrates them within a probabilistic model. RAS factor graph optimization (FGO) is developed as a robust localization method based on the prior probability distribution of the scaling factor and is derived from the maximum a posteriori (MAP) estimation to suppress the influence of abnormal measurements. A data-driven parameter calibration approach is also proposed to avoid manual tuning. Theoretical analysis is further conducted to examine the robustness of RAS FGO. The influence of the prior distribution parameters of the scaling factor on the suppression of abnormal measurement loss is investigated. It is also shown that the weighted least squares (WLSs) and the switchable constraints (SCs) methods are special cases of RAS FGO. Experimental results on the Google Smartphone Decimeter Challenge (GSDC) datasets demonstrate that the proposed method significantly outperforms traditional FGO in terms of both accuracy and robustness, particularly in scenarios with frequent measurement anomalies. In representative scenarios, the 3-D RMSE decreases from 18.87 to 4.77 m, which corresponds to a reduction of 74.72%. In addition, the 95% cumulative 3-D error decreases from 18.13 to 8.16 m, representing a reduction of 54.99%.
Keywords:
Adaptive scaling
factor graph optimization (FGO)
outlier suppression
robust positioning
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