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.

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.

On intersection volumes of confidence hyper-ellipsoids and two geometric Monte Carlo methods

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nima Rabiei ◽  
Elias G. Saleeby
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Numerical Experiments ◽  
High Probability ◽  
Statistical Decision ◽  
Time Series Clustering ◽  
Sample Mean ◽  
Wide Range ◽  
Geometric Type ◽  
Statistical Decision Making

Abstract The intersection or the overlap region of two n-dimensional ellipsoids plays an important role in statistical decision making in a number of applications. For instance, the intersection volume of two n-dimensional ellipsoids has been employed to define dissimilarity measures in time series clustering (see [M. Bakoben, T. Bellotti and N. M. Adams, Improving clustering performance by incorporating uncertainty, Pattern Recognit. Lett. 77 2016, 28–34]). Formulas for the intersection volumes of two n-dimensional ellipsoids are not known. In this article, we first derive exact formulas to determine the intersection volume of two hyper-ellipsoids satisfying a certain condition. Then we adapt and extend two geometric type Monte Carlo methods that in principle allow us to compute the intersection volume of any two generalized convex hyper-ellipsoids. Using the exact formulas, we evaluate the performance of the two Monte Carlo methods. Our numerical experiments show that sufficiently accurate estimates can be obtained for a reasonably wide range of n, and that the sample-mean method is more efficient. Finally, we develop an elementary fast Monte Carlo method to determine, with high probability, if two n-ellipsoids are separated or overlap.

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.

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