Probabilistic 3D Sound Source Mapping System Based on Monte Carlo Localization Using Microphone Array and LIDAR

2017 ◽  
Vol 29 (1) ◽  
pp. 94-104 ◽  
Author(s):  
Ryo Tanabe ◽  
◽  
Yoko Sasaki ◽  
Hiroshi Takemura ◽  
Keyword(s):  
Monte Carlo ◽  
Sound Source ◽  
Degrees Of Freedom ◽  
Microphone Array ◽  
Music Algorithm ◽  
Sensor Unit ◽  
3D Sound ◽  
Localization And Mapping ◽  
Mapping System ◽  
Monte Carlo Localization

[abstFig src='/00290001/09.jpg' width='300' text='3D sound source environmental map' ] The study proposes a probabilistic 3D sound source mapping system for a moving sensor unit. A microphone array is used for sound source localization and tracking based on the multiple signal classification (MUSIC) algorithm and a multiple-target tracking algorithm. Laser imaging detection and ranging (LIDAR) is used to generate a 3D geometric map and estimate the location of its six-degrees-of-freedom (6 DoF) using the state-of-the-art gyro-integrated iterative closest point simultaneous localization and mapping (G-ICP SLAM) method. Combining these modules provides sound detection in 3D global space for a moving robot. The sound position is then estimated using Monte Carlo localization from the time series of a tracked sound stream. The results of experiments using the hand-held sensor unit indicate that the method is effective for arbitrary motions of the sensor unit in environments with multiple sound sources.

scholarly journals MAPPING AND POSITIONING SYSTEM ON OMNIDIRECTIONAL ROBOT USING SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) METHOD BASED ON LIDAR

2021 ◽  
Vol 83 (6) ◽  
pp. 41-52
Author(s):  
Achmad Akmal Fikri ◽  
Lilik Anifah
Keyword(s):  
Monte Carlo ◽  
System Integration ◽  
Simultaneous Localization And Mapping ◽  
Autonomous Robot ◽  
Positioning System ◽  
Adaptive Monte Carlo ◽  
Surrounding Environment ◽  
Localization And Mapping ◽  
Mapping System ◽  
Monte Carlo Localization

The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research  are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

scholarly journals Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Matthias Morzfeld
Keyword(s):  
Monte Carlo ◽  
Numerical Experiments ◽  
High Probability ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Integral Control ◽  
Localization And Mapping ◽  
Monte Carlo Localization ◽  
Estimation And Control ◽  
And Control

Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the performance of algorithms that rely on Monte Carlo (MC) methods. Here the applicability and usefulness of implicit sampling for improving the performance of MC methods in estimation and control is explored, and implicit sampling based algorithms for stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM) are presented. The algorithms are tested in numerical experiments where it is found that fewer samples are required if implicit sampling is used, and that the overall runtimes of the algorithms are reduced.

Fly Ear Inspired Miniature Sound Source Localization Sensor: Localization in Two Dimensions

2010 ◽  
Author(s):  
Andrew P. Lisiewski ◽  
Haijun Liu ◽  
Miao Yu
Keyword(s):  
Sound Source ◽  
Degrees Of Freedom ◽  
Microphone Array ◽  
Micro Air Vehicles ◽  
Natural World ◽  
Two Dimensions ◽  
Directional Hearing ◽  
Directional Sensitivity ◽  
Seamless Integration ◽  
Mechanical Coupling

Miniature directional microphones are desirable and attractive in many applications including micro air vehicles, hearing aid devices, and anti-sniper systems. To overcome the size constraint, the natural world can serve as a source of inspiration. One striking example is found in the parasitoid fly Ormia Ochracea. Although the interaural distance of the fly ears is only 520μm, it can localize its cricket host with a resolution of as small as 2° [1]. The key to this remarkable directional hearing capability has been linked to a mechanical coupling between the fly’s two eardrums [1–3]. By mimicking the fly-ear design, two-membrane devices have been developed in our previous work [4] which can localize sound in one dimension. This work is intended to develop a three-membrane sound localization sensor, with a coupling beam connected between each of the two adjacent membranes. By utilizing the responses from all three membranes, this device can pinpoint a sound source based on the obtained bearing and elevation angles. A reduced-order model with three degrees of freedom has been developed, and parametric studies have been carried out to study the performance of the system. In experiment, the membrane responses have been detected by using a fiber optic interferometric system. The experimental results have demonstrated an improved directional sensitivity compared with that obtained from a conventional microphone array with uncoupled membranes. This work offers an entirely new approach for sensor design and development practice via the seamless integration of bio-inspired solutions, mechanics modeling, micro-fabrication techniques, and optical detection strategies.

It's only real if you see it: Surround system based 3D sound source modelling in virtual reality

2013 ◽  
Author(s):  
Agoston Torok ◽  
Daniel Mestre ◽  
Ferenc Honbolygo ◽  
Pierre Mallet ◽  
Jean-Marie Pergandi ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Sound Source ◽  
3D Sound

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

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