R-T-S Assisted Kalman Filtering for Robot Localization Using UWB Measurement

Sun, Mingxu; Gao, Yanli; Jiao, Zhuqing; Xu, Yuan; Zhuang, Yuan; Qian, Pengjiang

doi:10.1007/s11036-021-01902-6

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Anomaly detection method is a method of searching for outliers in a dataset. In previous studies on positioning data processing, most researchers focused on improving filtering algorithms or fusing multiple filtering algorithms [33,34]. These methods perform multiple filtering operations on the data to make the ranging data smoother to improve the positioning accuracy.…”

Section: Anomaly Detection Methodsmentioning

confidence: 99%

An improved positioning method based on compensation and optimization of ultra-wideband ranging results

Shi,

Wang,

Zeng

et al. 2024

Meas. Sci. Technol.

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Achieving precision in positioning under conditions of significant interference remains an unresolved challenge in research. This study introduces a low-cost Ultra-Wideband (UWB) distance compensation model that addresses electromagnetic wave loss in practical indoor settings. This paper employs kurtosis to detect Non-Line-of-Sight (NLOS) environments, which are frequently induced by pedestrian movement. The Generalized Extreme Studentized Deviate (GESD) algorithm is utilized to discern and eliminate outliers in ranging values and the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) algorithm compensates for the eliminated data points. Finally, Kalman filtering is used to improve UWB ranging results, allowing for better error elimination and compensation. Experimental results demonstrate that our proposed algorithm has higher accuracy and the mean square error improvement ratio can reach more than 20% in dynamic positioning tests.

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Section: Anomaly Detection Methodsmentioning

confidence: 99%

An improved positioning method based on compensation and optimization of ultra-wideband ranging results

Shi,

Wang,

Zeng

et al. 2024

Meas. Sci. Technol.

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“…As a comparison, the PF method and Rauch-Tung-Striebel (RTS) smoothing method [39] are introduced to demonstrate the performance of our proposed MPJE method. And the RTS smoother is used to improve the LSE results, named as LSE-RTS.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Acoustic Indoor Localization Based on Range and Relative Velocity in Non-Line-of-Sight Environment

Zhang,

Wang,

et al. 2024

IEEE Sensors J.

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This paper investigates a problem of acoustic indoor localization in dense Non-Line-of-Sight (NLOS) environments by using range and relative velocity measurements obtained from received acoustic signals, without the need of additional sensors. The relationship between adjacent positions to be estimated is established by introducing velocity measurements and assuming a first-order motion model. This reduces the number of required Line-of-Sight (LOS) anchors. The principle of the range and relative velocity-based localization method is systematically studied and a basic solution based on a Least Square estimator (LSE) and its closed-form has been proposed. In order to achieve precise localization in dense NLOS environments, a multi-position joint estimation (MPJE) method is proposed. This method involves jointly estimating multiple positions in a short-time position sequence and is solved using a Levenberg-Marquardt (LM) algorithm. A LOS measurements redundancy metric is proposed to balance the localization accuracy and algorithm time cost by adjusting the sequence length. The results obtained from numerical simulations and experimental investigations demonstrate that the proposed range and relative velocity-based localization method outperforms the conventional range-based methods. The number of required LOS anchors for accurate localization is reduced from 3 to 2 for 2D positioning. Additionally, the proposed multi-position joint estimation method requires only 2 LOS anchors and permits the use of only 1 LOS anchor for a short period. This enables its application in dense NLOS environments with a low anchor deployment density, thereby increasing its scene adaptive capability and promotional value.

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“…The most difficult one is global localization because of no initial pose information. Kalman filter and its extended versions are mainly used for pose tracking, not suitable for the other two problems [20][21][22]. In contrast, the particle filter is compatible with all the three cases [23].…”

Section: Laser-slam and Localizationmentioning

confidence: 99%

Mobile Robot Localization in Geometrically Similar Environment Combining Wi-Fi with Laser SLAM

2023

KSII TIIS

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Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both 1340 Ge et al.: Mobile Robot Localization in Geometrically Similar Environment Combining Wi-Fi with Laser SLAM global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

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R-T-S Assisted Kalman Filtering for Robot Localization Using UWB Measurement

Cited by 6 publications

References 23 publications

An improved positioning method based on compensation and optimization of ultra-wideband ranging results

An improved positioning method based on compensation and optimization of ultra-wideband ranging results

Acoustic Indoor Localization Based on Range and Relative Velocity in Non-Line-of-Sight Environment

Mobile Robot Localization in Geometrically Similar Environment Combining Wi-Fi with Laser SLAM

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