A Fast Algorithm of Simultaneous Localization and Mapping for Mobile Robot Based on Ball Particle Filter

Luo, Jingwen; Qin, Shiyin

doi:10.1109/access.2018.2819419

Cited by 22 publications

(12 citation statements)

References 35 publications

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“…To solve this issue, the PDR estimated position adjustment algorithm based on selective particle filtering is adopted to adjust the estimated position of crowdsourced samples, which is mainly used to calibrate PDR-based user position by performing progressive selective particle filtering [33][34][35][36] and using the estimated position based on fingerprint. The step lengths, steps, and orientations of pedestrians are measured and counted [37] to calculate their walking trajectories and current positions.…”

Section: Pdr Estimated Position Adjustment (Pepa)mentioning

confidence: 99%

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Guo

Qin

Zhao

et al. 2020

Sensors

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The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

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Section: Pdr Estimated Position Adjustment (Pepa)mentioning

confidence: 99%

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Guo

Qin

Zhao

et al. 2020

Sensors

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“…Therefore, another approach called the light detection and ranging (LiDAR) has been developed to achieve higher positioning resolution in environments containing many features [10]. An example of such a solution is an improved LiDAR-based localization algorithm for mobile robots proposed in [11].…”

Section: Rfid-based Localization Schemes This Approach Relies Onmentioning

confidence: 99%

Indoor INS/LiDAR-Based Robot Localization With Improved Robustness Using Cascaded FIR Filter

Shmaliy

et al. 2019

IEEE Access

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In this paper, an indoor inertial navigation system (INS) integrated with light detection and ranging (LiDAR) robot localization system is proposed to provide accurate information about the robot location. To achieve high accuracy and robustness, a cascaded finite-impulse response (FIR) filter is designed and incorporated into the proposed INS/LiDAR localization scheme. The cascaded scheme employs two FIR filters. An unbiased FIR filter is used to estimate the LiDAR-derived position by fusing distances between a robot and detected corner points. An extended FIR filter is used to fuse the LiDAR-and INS-based measurements. An experimental study indicates that the proposed scheme demonstrates higher robustness than the traditional methods of localization employing Kalman filtering. INDEX TERMS Indoor localization, inertial navigation system, light detection and ranging, FIR filtering, Kalman filtering.

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“…However, to the best of the author's knowledge, they are either limited to a specific state representation, e.g. [2,16,5], fully observable states [14] or are not determinisitic [7].…”

Section: Resampling Stepmentioning

confidence: 99%

A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements

et al. 2019

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The first stage in any control system is to be able to accurately estimate the system's state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems. Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error. Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, that of Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), Monte Carlo Markov Chain (MCMC), and the original Box Particle Filter (BPF). The algorithm outperforms existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty. The BRPF reduces the computational load by 73% and 90% for SIR-PF and MCMC, respectively, with similar RMSE values. This work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods.

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A Fast Algorithm of Simultaneous Localization and Mapping for Mobile Robot Based on Ball Particle Filter

Cited by 22 publications

References 35 publications

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Indoor INS/LiDAR-Based Robot Localization With Improved Robustness Using Cascaded FIR Filter

A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements

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