Mobile Robot Localization using Particle Filters and Sonar Sensors

Burguera, Antoni; González, Yolanda; Oliver, Gabriel

doi:10.5772/39415

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…One can conclude that above a certain threshold, increasing the number of particles is not determinant for the particle filtering behavior. This is reminiscent of observations made by Burguera et.al in [31]. A priori initial position (μ x,0 vs. x 0 ).…”

Section: Influence Of the Parameterssupporting

confidence: 50%

Pass-by noise acoustic sensing for estimating speed and wheelbase length of two-axle vehicles.

Marmaroli¹,

Odobez²,

Falourd³

et al. 2013

Proceedings of Meetings on Acoustics

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This paper focuses on acoustic road traffic monitoring and looks, more specifically, into the problem of joint speed and wheelbase length estimation of two-axle vehicles as they pass by. It is known that both front and rear axle trajectories may be observed using a cross-correlation based processing in conjunction with a well designed two-element microphone array placed on the roadside. This is mainly due to the broadband nature of the tyre/road noise which makes two peaks appear in, one per axle, in the correlation function. In a former work, we proposed to conduct this double-peak-tracking problem using a specific particle filter that model road vehicles as bimodal sound sources (bimodal particle filter). After a brief theoretical recall of the method, this paper stresses on the recent preliminary results we obtained from simulation and in-situ experiments.

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Section: Influence Of the Parameterssupporting

confidence: 50%

Pass-by noise acoustic sensing for estimating speed and wheelbase length of two-axle vehicles.

Marmaroli¹,

Odobez²,

Falourd³

et al. 2013

Proceedings of Meetings on Acoustics

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show abstract

“…Bayes filter is the most general approach to compute beliefs based on observations, action data and prior probability. However, in terms of the computational tractability, the general Bayes filter is not trackable for continuous state spaces [3]. The classical trackable solution in probabilistic methods is the Kalman Filter [4], this recursive approach to the discrete-data linear filtering problem based on Gaussian filters has been the subject of extensive research in the SLAM.…”

Section: Particle Filter Conceptmentioning

confidence: 99%

Adaptive particle filter for localization problem in service robotics

et al. 2018

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Abstract. In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.

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“…Although the method was only tested in simulation, this approach does not require any a priori map and exhibits very good results. A similar approach, tested using real sonar readings, was proposed in [ 14 ]. Although both approaches share some points in common with the research presented in this paper, they neither experimentally characterize the sonar sensor nor model it in any way.…”

Section: Introductionmentioning

confidence: 99%

Sonar Sensor Models and Their Application to Mobile Robot Localization

Burguera

González

Oliver

2009

Sensors

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This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.

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Mobile Robot Localization using Particle Filters and Sonar Sensors

Cited by 8 publications

References 28 publications

Pass-by noise acoustic sensing for estimating speed and wheelbase length of two-axle vehicles.

Pass-by noise acoustic sensing for estimating speed and wheelbase length of two-axle vehicles.

Adaptive particle filter for localization problem in service robotics

Sonar Sensor Models and Their Application to Mobile Robot Localization

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