Relax and Recover: Guaranteed Range-Only Continuous Localization

Pacholska, Michalina; Dümbgen, Frederike; Scholefield, Adam

doi:10.1109/lra.2020.2970952

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Futhermore, non-linear optimization technology is also proposed for VLP positioning in [8], which globally optimizes the positioning observations. The work [1] proposed a path optimization algorithm based on distance observations-Relax and Recover (RAR), which restores the robot's trajectory using curve fitting. This method eliminated the ranging error caused by the delay using encrypting the observation value, and conducted the positioning when the number of observations was small.…”

Section: Related Workmentioning

confidence: 99%

“…The essence of the Relax and Recover (RAR) algorithm [1] is to restore multiple observations of each epoch (there is a time delay, but they are different epochs) into a single observation of multiple epochs, and each observation corresponds to a certain position during the movement of the robot. All observations are used for curve fitting to restore the robot's trajectory.…”

Section: The Principle Of the Rar Algorithmmentioning

confidence: 99%

“…A path optimization algorithm based on distance observations-Relax and Recover (RAR) was proposed to restore the robot's trajectory using curve fitting [1]. This article mainly improves the RAR algorithm for the VLP system.…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

WANG

Tan

2023

IEICE Trans. Inf. & Syst.

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The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the antigross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.

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Section: Related Workmentioning

confidence: 99%

Section: The Principle Of the Rar Algorithmmentioning

confidence: 99%

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High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

WANG

Tan

2023

IEICE Trans. Inf. & Syst.

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“…In range coordinates, the motion model corresponding to the NCV model in Cartesian coordinates has been presented in [14], where the state vector is defined as…”

Section: Problem Statementmentioning

confidence: 99%

State estimation in range coordinate using range-only measurements

Guo

Zhou

2022

J. of Syst. Eng. Electron.

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In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

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“…This requires handling association of range measurements, which is performed using a probabilistic method. Pacholska et al [16] tackle the rangeonly localization problem by employing a parametric model of the vehicle trajectory, and formulate the recovery as a quadratic optimization problem, dependent on the trajectory parameters and range measurements, whose uncertainty is assumed to follow a zero-mean Gaussian. This problem is then relaxed to a linear form, and conditions are presented for the successful recovery of trajectories.…”

Section: Related Workmentioning

confidence: 99%

Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation

Fourie

Rypkema

Teixeira

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense -a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a realworld AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements.

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Relax and Recover: Guaranteed Range-Only Continuous Localization

Cited by 15 publications

References 26 publications

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

State estimation in range coordinate using range-only measurements

Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation

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