Optimization of Rao-Blackwellized Particle Filter in Activity Pedestrian Simultaneously Localization and Mapping (SLAM): An Initial Proposal

Bejuri, Wan Mohd Yaakob Wan; Mohamad, Mohd Murtadha; Raja, Raja Zahilah; Radzi, Mohd

doi:10.14257/ijsia.2015.9.11.35

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…However, identical observation data is retained at every time step, and there will the same starting quantity of (17) |N − N * | =: Table 1 Parameter setting…”

Section: Resultsmentioning

confidence: 99%

“…In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights:…”

Section: Introductionmentioning

confidence: 99%

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Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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“…However, identical observation data is retained at every time step, and there will the same starting quantity of (17) |N − N * | =: Table 1 Parameter setting…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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“…The inertial sensor can be used to determine user location wherever the user may be without having to use extra building infrastructure as the sensor is already found in many types of smart phones used today [3]. Some examples of inertial-based mobile IPS include mobile asset navigation, mobile first-responder navigation and mobile emergency rescue and tracking [4][5] [6] [7]. One of the major issues of mobile inertial navigation systems is sample impoverishment during particle filtering.…”

Section: Introductionmentioning

confidence: 99%

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

Bejuri*,

Mohamad,

Omar

et al. 2019

IJITEE

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The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

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“…With the rapid advancement in location technologies and tracking devices, and the demand for flawless solutions to overcome the problems associated with current mobile location based techniques, there is widespread interest in mobile IPS systems (IPS) [1][2][3][4][5][6][7][8][9][10][11][12][13]. One of the major components of mobile IPS is inertial-based positioning, which facilitates the tracing of individuals (or mobile nodes) within corridors or other enclosed structures by using inertial sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The inertial sensor can be used to determine user location everywhere and ubiquitous without using extra building infrastructure and it equipped in many smart phone nowadays [14][15][16][17][18][19]. Some examples of inertial-based mobile IPS include mobile asset navigation, mobile first-responder navigation and mobile emergency rescue and tracking [20][21][22][23].One of the major issue of inertial-based mobile IPS, is sample impoverishment during particle filtering. This phenomenon will cause computation burden to the overall systems [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Special Strategies Resampling for Inertial-based Mobile Indoor Positioning Systems: An Initial Proposal

Bejuri¹,

Mohamad²,

Radzi³

et al. 2017

IJCA

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The phenomenon of sample impoverishment during particle filtering always contribute computation burden to the inertial-based mobile IPS systems. This is due to the factor of noise measurement and number of particle. Usually, the special strategies resampling algorithms was used. However, these algorithms just can fit in certain environment. This needs an adaptation of noise measurement and number of particle in a algorithm in order to make resampling with more intelligence, reliability and robust. In tbis paper, we will propose an adaptive special strategies resampling by adapting noise and particle measurement. These adaptation is used to determine the most suitable algorithm of special strategies resampling, that can be switched for resampling purpose. Finally, we illustrate our proposed solution our for indoor environment setup.

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Optimization of Rao-Blackwellized Particle Filter in Activity Pedestrian Simultaneously Localization and Mapping (SLAM): An Initial Proposal

Abstract: Abstract

Cited by 5 publications

References 24 publications

Adaptive memory-based single distribution resampling for particle filter

Adaptive memory-based single distribution resampling for particle filter

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

Adaptive Special Strategies Resampling for Inertial-based Mobile Indoor Positioning Systems: An Initial Proposal

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