Particle Filters For Robot Navigation
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Particle Filters For Robot Navigation
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Author : Cyrill Stachniss
language : en
Publisher:
Release Date : 2014
Particle Filters For Robot Navigation written by Cyrill Stachniss and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Autonomous robots categories.
Autonomous navigation is an essential capability for mobile robots. In order to operate robustly, a robot needs to know what the environment looks like, where it is in its environment, and how to navigate in it. This work summarizes approaches that address these three problems and that use particle filters as their main underlying model for representing beliefs. We illustrate that these filters are powerful tools that can robustly estimate the state of the robot and its environment and that it is also well-suited to make decisions about how to navigate in order to minimize the uncertainty of the joint belief about the robot's position and the state of the environment.
Robot Navigation Using Velocity Potential Fields And Particle Filters For Obstacle Avoidance
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Author : Jin Bai
language : en
Publisher:
Release Date : 2015
Robot Navigation Using Velocity Potential Fields And Particle Filters For Obstacle Avoidance written by Jin Bai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
An Integrated Approach To Robotic Navigation Under Uncertainty
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Author : Bin Wu
language : en
Publisher:
Release Date : 2011
An Integrated Approach To Robotic Navigation Under Uncertainty written by Bin Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Autonomous robot navigation has been gaining popularity in the field of robotics research due to its important and broad applications. Sequential Monte Carlo methods, also known as particle filters, are a class of sophisticated Bayesian filters for nonlinear/non-Gaussian model estimation, and have been used for the simultaneous localization and mapping (SLAM) problem in robot navigation in lieu of extended Kalman filters. However, the current particle filters, and their derivatives such as the particle-based SLAM filters for robotic navigation, still need further improvement to have better trade-off between performance and complexity in order to be used for online applications. Also, the current robot navigation approaches often focus on one aspect of the problem, lacking an integrated structure. In this work, we designed better sampling proposal distributions for particle filters, and demonstrated their superiority in simulation. Then, we applied our new particle filters to design and implement improved particle-based SLAM filters for the application of the SLAM problem in robot navigation, and tested using both simulation and outdoor experimental datasets. Finally, we incorporated the new particle-based SLAM filters in the design of a new framework for solving robotic navigation problems under uncertainty in a continuous environment. The framework balances between exploration and exploitation, and integrates global planning algorithms, local navigation routines, and exploration procedures in order to achieve the global goal, overcoming many common drawbacks of current approaches.
Multi Threaded Implementation Of Particle Filters
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Author : Tanmay Misra
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2014-10-24
Multi Threaded Implementation Of Particle Filters written by Tanmay Misra and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-24 with categories.
Particle filter (PF) based state estimation techniques have been proposed for numerous problems in robotics, computer vision, navigation etc. The accuracy of these algorithms depends on the number of particles employed to represent the probability density function, however, as the number of particles increases so does the computational cost of the algorithm, thereby limiting its usefulness in many real-time problems. Thus, there is always a trade-off between the required accuracy and computational efficiency in using such algorithms. This work implements a parallelized particle filter algorithm for multi-core processors to reduce the total processing time. The specific algorithm studied is the Monte Carlo Localization (MCL), a PF method for mobile robot localization. The multi-threaded version of MCL significantly improves the computational performance of the algorithm compared to its sequential execution. The results are compared with Amdahl's law which predicts the theoretical maximum speedup using multiple processors.The methodology used in this work can serve as a general framework for similar algorithms and applications.
Particle Filters For Random Set Models
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Author : Branko Ristic
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-15
Particle Filters For Random Set Models written by Branko Ristic and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-15 with Technology & Engineering categories.
This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
Particle Filter Based Slam To Map Random Environments Using Irobot Roomba
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Author : Akash Patki
language : en
Publisher:
Release Date : 2011
Particle Filter Based Slam To Map Random Environments Using Irobot Roomba written by Akash Patki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Electronic dissertations categories.
Adaptive Particle Filters For High Signal To Noise Ratios With Applications To Robotics
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Author : Suk Won Chung
language : en
Publisher:
Release Date : 2015
Adaptive Particle Filters For High Signal To Noise Ratios With Applications To Robotics written by Suk Won Chung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Dynamic state-space models are useful for describing data in various fields, including robotics. An important problem that may be solved by using dynamic state-space models is the estimation of underlying state processes from given observations. When the models are non-linear and the noise not Gaussian, it is impossible to solve the problem analytically; thus, particle filters, also known as sequential Monte Carlo methods, tend to be employed. However, because particle filters are based on sequential importance sampling, the problem arises of how to select the importance density function. Handling unknown parameters in the model presents another significant difficulty in particle filtering. Simultaneous localization and mapping (SLAM) in robotics is one well-known but difficult problem for which particle filters have been used. This dissertation is motivated by SLAM problems and related particle filtering approaches. In this dissertation, we design a new proposal distribution that better approximates the optimal importance function, using a novel way of combining information from observations and state transition dynamics. In the first part of our study, after reviewing representative approaches for SLAM problems, we justify our method of combining information with a series of examples and offer an efficient means of constructing the new proposal distribution. In the second part, we focus on the problems inherent in handling unknown parameters in state-space models. We suggest the application of one-step recursive expectation-maximization (EM) algorithm to learn unknown parameters, and recommend pairing it with the new proposal distribution into an adaptive particle filter algorithm. Furthermore, we propose a new SLAM filter based on the adaptation of the new adaptive particle filter to SLAM problems. In Chapter 3, we conduct simulation studies on localization and SLAM problems to demonstrate the superior numerical performance of the proposed algorithms.
Beyond The Kalman Filter Particle Filters For Tracking Applications
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Author : Branko Ristic
language : en
Publisher: Artech House
Release Date : 2003-12-01
Beyond The Kalman Filter Particle Filters For Tracking Applications written by Branko Ristic and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-01 with Technology & Engineering categories.
For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Particle Filters For Positioning Navigation And Tracking
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Author :
language : en
Publisher:
Release Date : 2001
Particle Filters For Positioning Navigation And Tracking written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Particle Filter For Underwater Terrain Navigation
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Author : Rickard Karlsson
language : en
Publisher:
Release Date : 2003
Particle Filter For Underwater Terrain Navigation written by Rickard Karlsson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.