Getting The Best of Particle and Kalman Filters: GNSS Sensor Fusion using Rao-Blackwellized Particle Filter

Gupta, Shubh; Mohanty, Adyasha; Gao, Grace Xingxin

doi:10.33012/2022.18470

Cited by 3 publications

(2 citation statements)

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“…This decomposition and combination approach can effectively reduce computational complexity while providing more precise state estimation, particularly in high-dimensional and non-linear systems [32]. Gupta et al employed RBPF for the fusion of GNSS and visual odometer, which combines the tracking efficiency of KF with the superior uncertainty modeling of PF, enabling effective state tracking and rich position probability distribution [33]. Norhidayah et al adopted RBPF in a grid-based Simultaneous Localization and Mapping (SLAM) algorithm, effectively improving the mapping accuracy of the map and significantly reducing the error in robot state estimation [34].…”

Section: Introductionmentioning

confidence: 99%

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Jia,

Wang,

et al. 2023

Sensors

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To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot’s pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

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Section: Introductionmentioning

confidence: 99%

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Jia,

Wang,

et al. 2023

Sensors

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show abstract

“…This facilitates the reuse of the standard filtering components in the EKF for efficient implementation [31]. We then apply this filter to a multi-sensor setup comprising of camera, GNSS, and Attitude and Heading Reference System (AHRS), where the sensor modules are developed based on our previous work [32]. However, tuning the parameters for our complex multi-sensor setup is challenging.…”

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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Urban localization using robust filtering at multiple linearization points

Gupta,

Mohanty,

Gao

2023

EURASIP J. Adv. Signal Process.

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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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Getting The Best of Particle and Kalman Filters: GNSS Sensor Fusion using Rao-Blackwellized Particle Filter

Cited by 3 publications

References 0 publications

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Urban Localization using Robust Filtering at Multiple Linearization Points

Urban localization using robust filtering at multiple linearization points

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