Development of Rao-Blackwellized Particle Filter (RBPF) SLAM Algorithm Using Low Proximity Infrared Sensors

Yatim, Norhidayah Mohamad; Buniyamin, Norlida

doi:10.1007/978-981-10-1721-6_43

Cited by 6 publications

(17 citation statements)

References 10 publications

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“…However, higher number of particles might suffer from forbidding memory burden and higher computational cost. This problem can be overcome by integrating the SLAM technique with an artificial neural network (ANN) while using low-cost sensors [5], [19], [20], [33]. The noisy dataset from the sensor of the mobile robot are used to train the ANN learner.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, there are two strategies of dataset that have been reviewed to train the ANN network. Firstly, the training network using the position of each of the grid cells of OGM [5], [19], [33]. Secondly, by using the distance from sensor to obstacles [20].…”

Section: Related Workmentioning

confidence: 99%

“…This problem can be solved by integrating the SLAM technique which is RBPF algorithm with an artificial neural network (ANN) while using low-cost sensors. ANN has been used with occupancy grid maps to interpret noisy sensor measurements and achieved better accuracy of the map [5], [19], [20]. Furthermore, [19] and [5] also shows the number of particles can be reduced with better accuracy of the sensor measurement.…”

Section: Introductionmentioning

confidence: 99%

“…ANN has been used with occupancy grid maps to interpret noisy sensor measurements and achieved better accuracy of the map [5], [19], [20]. Furthermore, [19] and [5] also shows the number of particles can be reduced with better accuracy of the sensor measurement. Therefore, ANN integrated with RBPF algorithm can reduce the computational cost of the particle filter and achieve better accuracy of OGM.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Artificial Neural Network Towards the Number of Particles of Rao-Blackwellized Particle Filter using Laser Distance Sensor

Jamaludin¹,

Yatim²,

Noh³

2023

IJACSA

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Rao-Blackwellized particle filter (RBPF) algorithm aims to solve the Simultaneous Localization and Mapping(SLAM) problem. The performance of RBPF is based on the number of particles. The higher the number of particles, the better the performance of RBPF. However, higher number of particles required high memory and computational cost. Nevertheless, the number of particles can be reduced by using high-end sensor. By using high-end sensor, high performance of RBPF can be achieved and reduced the number of particles. But the development of the robot came at a high cost. A robot can be equipped with low-cost sensor in order to reduce the overall cost of the robot. However, low-cost sensor presented challenges of creating good map accuracy due to the low accuracy of the sensor measurement. For that reason, RBPF is integrated with artificial neural network (ANN) to interpret noisy sensor measurements and achieved better accuracy in SLAM. In this paper, RBPF integrated with ANN is experimented by using Turtlebot3 in real-world experiment. The experiment is evaluated by comparing the resulting maps estimated by RBPF with ANN and RBPF without ANN. The results show that RBPF with ANN has increased the performance of SLAM by 25.17% and achieved 10 out of 10 trials of closed loop map by using only 30 particles compared to RBPF without ANN that needs 400 particles to achieve closed loop map. In conclusion, it shows that, SLAM performance can be improved by integrating RBPF algorithm with ANN and reduces the number of particles.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Artificial Neural Network Towards the Number of Particles of Rao-Blackwellized Particle Filter using Laser Distance Sensor

Jamaludin¹,

Yatim²,

Noh³

2023

IJACSA

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“…In the recent past, several publications have proposed approaches for localization [39,40] and trajectory tracking [38,41] that are based on the MPF because of its advantages for mixed linear/nonlinear systems. Automotive use cases include a road target tracking application, of which the multimodality requires using a PF or MPF [42].…”

Section: Bayesian Filtersmentioning

confidence: 99%

An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization

et al. 2019

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The B-spline function representation is commonly used for data approximation and trajectory definition, but filter-based methods for NWLS approximation are restricted to a bounded definition range. We present an algorithm termed NRBA for an iterative NWLS approximation of an unbounded set of data points by a B-spline function. NRBA is based on a MPF, in which a KF solves the linear subproblem optimally while a PF deals with nonlinear approximation goals. NRBA can adjust the bounded definition range of the approximating B-spline function during run-time such that, regardless of the initially chosen definition range, all data points can be processed. In numerical experiments, NRBA achieves approximation results close to those of the Levenberg–Marquardt algorithm. An NWLS approximation problem is a nonlinear optimization problem. The direct trajectory optimization approach also leads to a nonlinear problem. The computational effort of most solution methods grows exponentially with the trajectory length. We demonstrate how NRBA can be applied for a multiobjective trajectory optimization for a BEV in order to determine an energy-efficient velocity trajectory. With NRBA, the effort increases only linearly with the processed data points and the trajectory length.

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Design and simulation of disaster mitigation robot using machine vision

Avinaash

Andrew

Visnu

et al. 2021

J. Phys.: Conf. Ser.

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The goal is to provide a robust mobile robot to assist recures in their operation by providing real time surveillance, remote operation as well autonomous operation. The mobile robot is capable of mapping using a LIDAR, this provides a map of the disaster zone which is then used to assess and provide a path planning for the most efficient rescue for the Mobile robot as well as the Rescuers. The built-in gas sensor reads the concentration of gases in the area, providing data on the environment in the disaster zone. The use of high definition cameras combined with Artificial Intelligence based image classification provides an edge in autonomous traversing and search and rescue. Disaster mitigation is an extremely dangerous task for the rescuers, a mobile robot such as LV-01 can be the difference between life and death.

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Development of Rao-Blackwellized Particle Filter (RBPF) SLAM Algorithm Using Low Proximity Infrared Sensors

Cited by 6 publications

References 10 publications

The Effect of Artificial Neural Network Towards the Number of Particles of Rao-Blackwellized Particle Filter using Laser Distance Sensor

The Effect of Artificial Neural Network Towards the Number of Particles of Rao-Blackwellized Particle Filter using Laser Distance Sensor

An Iterative Method Based on the Marginalized Particle Filter for Nonlinear B-Spline Data Approximation and Trajectory Optimization

Design and simulation of disaster mitigation robot using machine vision

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