Adaptive complementary filtering algorithm for mobile robot localization

Neto, Armando Alves; Macharet, Douglas G.; Campos, Víctor Costa da Silva; Campos, Mário F. M.

doi:10.1007/bf03194503

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…It is to be noted that λ may vary from time and can be adjusted automatically according to the different motion state of the pedestrian. To achieve the balance of search speed and estimation acc uracy of the orientation, considerable work has been conducted to solve this problem [16,18]. The differ ential equation of the attitude of the gyroscope is expressed as follows:…”

Section: Acfmentioning

confidence: 99%

“…where I is the unit matrix. Thus, the approximate fusion quaternion of the information is obtained, namely Q new , as shown in (18),…”

Section: Acfmentioning

confidence: 99%

“…In the following, we will introduce several state-of-the-art algorithms in relation to orientation estimation. The complementary filter (CF) algorithm [15][16][17][18] has yielded success since the three sensors have complementary error characteristics-the gyroscope gives poor long-term orientation, while the magnetometer and accelerometer are subject to short-term orientation errors. The Kalman filter (KF) is a minimumvariance state estimator for a linear dynamic system with Gaussian noise [19,20].…”

Section: Introductionmentioning

confidence: 99%

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An orientation estimation algorithm based on multi-source information fusion

Shi

Xun

Chen

et al. 2018

Meas. Sci. Technol.

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With the development of the Internet-of-Things, urgent market demands have also provided a huge development space for indoor pedestrian positioning systems (IPPS). Complementary filters (CF), Kalman filters and their various modifications are usually used to estimate the orientation in foot-mounted IPPS. However, the accuracy of the orientation is still low because of various types of electromagnetic interference and the diversity of pedestrians’ motion states. In this paper, an orientation estimation algorithm is proposed innovatively. First, the nine-axes inertial/magnetic sensors are calibrated using the error model of micro-electromechanical systems sensors. Then, the adaptive complementary filtering (ACF) algorithm is used to determine short- and medium-term orientation by making full use of the complementary error characteristics of the nine-axes inertial/magnetic sensors; the corridor geometry in rectangular buildings can also be regarded as prior information. Finally, the fusion of the orientation calculated by the ACF algorithm and the prior information is realized by the extended Kalman filter algorithm, which can improve the long-term accuracy of the orientation. To verify the performance of the proposed algorithm, a foot-mounted IPPS is set up based on MPU9250, and the raw data from the sensors are calibrated and imported into MATLAB for processing and analysis. The root mean square error of the orientation is less than 1.5° using the proposed method, and the results show that the proposed algorithm significantly surpasses other state-of-the-art algorithms, contributing to higher accuracy of orientation.

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Section: Acfmentioning

confidence: 99%

“…where I is the unit matrix. Thus, the approximate fusion quaternion of the information is obtained, namely Q new , as shown in (18),…”

Section: Acfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An orientation estimation algorithm based on multi-source information fusion

Shi

Xun

Chen

et al. 2018

Meas. Sci. Technol.

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“…In view of the steadiness of track alignment positioning, a simplified approach is introduced to remove the necessity of error propagation. The complementary filter is a possible alternative to Kalman or Particle filters to minimise the computational complexity (Neto et al, 2009), which is configured with low-pass and high-pass filters for data fusion:…”

Section: Processing Alternativementioning

confidence: 99%

A Method for the Positioning and Orientation of Rail-Bound Vehicles in GNSS-Free Environments

Hung¹,

King²,

Chen³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mobile Mapping System (MMS) are increasingly applied for spatial data collection to support different fields because of their efficiencies and the levels of detail they can provide. The Position and Orientation System (POS), which is conventionally employed for locating and orienting MMS, allows direct georeferencing of spatial data in real-time. Since the performance of a POS depends on both the Inertial Navigation System (INS) and the Global Navigation Satellite System (GNSS), poor GNSS conditions, such as in long tunnels and underground, introduce the necessity for post-processing. In above-ground railways, mobile mapping technology is employed with high performance sensors for finite usage, which has considerable potential for enhancing railway safety and management in real-time. In contrast, underground railways present a challenge for a conventional POS thus alternative configurations are necessary to maintain data accuracy and alleviate the need for post-processing. This paper introduces a method of rail-bound navigation to replace the role of GNSS for railway applications. The proposed method integrates INS and track alignment data for environment-independent navigation and reduces the demand of post-processing. The principle of rail-bound navigation is presented and its performance is verified by an experiment using a consumer-grade Inertial Measurement Unit (IMU) and a smallscale railway model. The method produced a substantial improvement in position and orientation for a poorly initialised system in centimetre positional accuracy. The potential improvements indicated by, and limitations of rail-bound navigation are also considered for further development in existing railway systems.

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“…In [16] and [17] adaptive EKFs are proposed, where the adaptation is based on the deviation of the actual from the expected measurement and the same deviation used in a fuzzy logic setting, respectively. Adaptive complementary filters (ACFs) have also been used as in [18], [19] and [20], where adaptation based on comparing the accelerometer with the gravity vector is used. Furthermore, [21] provides a complementary filter in the least squares sense (also known as Wahba's problem [22]), using the difference between the output of the filter and the measurement by the accelerometer for adaptation and [23] proposes a multiple model adaptive complementary filter, with adaptation based on the same measure.…”

Section: Introductionmentioning

confidence: 99%

Similarity-Based Adaptive Complementary Filter for IMU Fusion

Andrien

Antunes

Molengraft

et al. 2018

2018 European Control Conference (ECC)

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Adaptive complementary filtering algorithm for mobile robot localization

Cited by 14 publications

References 7 publications

An orientation estimation algorithm based on multi-source information fusion

An orientation estimation algorithm based on multi-source information fusion

A Method for the Positioning and Orientation of Rail-Bound Vehicles in GNSS-Free Environments

Similarity-Based Adaptive Complementary Filter for IMU Fusion

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