Robust Kalman Filter Enhanced by Projection Statistic Detector for Multisensor Navigation in Urban Canyon Environment

Jiang, Yingying; Pan, Shuguo; Meng, Qian; Gao, Wang; Yu, Baoguo; Jia, Fengshuo

doi:10.1109/jsen.2022.3230708

Cited by 4 publications

(1 citation statement)

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“…These systems fuse data from a diverse array of complementary sensors, thereby mitigating individual sensor limitations and leading to a more reliable estimate of the state. However, integrating multi-sensor data presents its own set of challenges, such as the need to mitigate large errors in sensor measurements and accommodate diverse noise sources [5,6]. These diverse noise sources contribute to multiple modes of uncertainty that may yield multiple plausible solutions for the state.…”

Section: Introductionmentioning

confidence: 99%

Urban Localization using Robust Filtering at Multiple Linearization Points

Gupta

Mohanty

Gao

2023

Preprint

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We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao-Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.

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Section: Introductionmentioning

confidence: 99%