Robot Position Estimation and Tracking Using the Particle Filter and SOM in Robotic Space

2006 ◽  
pp. 530-539 ◽  
Author(s):  
TaeSeok Jin ◽  
JangMyung Lee
Keyword(s):  
Particle Filter ◽  
Position Estimation ◽  
Robot Position

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

Robot position estimation using range and compass data

1992 ◽  
Author(s):  
Raashid Malik ◽  
Braham Benteftifa
Keyword(s):  
Position Estimation ◽  
Robot Position

scholarly journals State Estimation for Mobile Robot using Partially Observable Markov Decision Process

1996 ◽  
Vol 8 (3) ◽  
pp. 272-277
Author(s):  
Daehee Kang ◽  
◽  
Hideki Hashimoto ◽  
Fumio Harashima
Keyword(s):  
Mobile Robot ◽  
Markov Decision Process ◽  
Decision Process ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Estimation Errors ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Robot Position

Dead Reckoning has been commonly used for position estimation. However, this method has inherent problems, one of the biggest being it always cumulates estimation errors. In this paper, we propose a new method to estimate a current mobile robot state using Partially Observable Markov Decision Process (POMDP). POMDP generalizes the Markov Decision Process (MDP) framework to the case where the agent must make its decisions in partial ignorance of its current situation. Here, the robot state means the robot position or current subgoal at which the mobile robot is located. It is shown that we will be able to estimate the mobile robot state precisely and robustly, even if the environment is changed slightly, through a case study.

scholarly journals Particle Filter and Finite Impulse Response Filter Fusion and Hector SLAM to Improve the Performance of Robot Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amin Bassiri ◽  
Mohammadreza Asghari Oskoei ◽  
Anahid Basiri
Keyword(s):  
Particle Filter ◽  
Impulse Response ◽  
Hybrid Algorithm ◽  
Finite Impulse Response ◽  
Sampling Rate ◽  
Position Estimation ◽  
Laser Sensor ◽  
Indoor Environments ◽  
Hybrid Filter ◽  
Signal Ratio

Indoor position estimation is essential for navigation; however, it is a challenging task mainly due to the indoor environments’ (a) high noise to signal ratio and (b) low sampling rate and (c) sudden changes to the environments. This paper uses a hybrid filter algorithm for the indoor positioning system for robot navigation integrating Particle Filter (PF) algorithm and Finite Impulse Response (FIR) filter algorithm to assure the continuity of the positioning solution. Additionally, the Hector Simultaneous Localisation and Mapping (Hector SLAM) algorithm is used to map the environment and improve the accuracy of the navigation. The paper implements the hybrid algorithm that uses the integrated PF, FIR, and Hector SLAM, using an embedded laser scanner sensor. The hybrid algorithm coupled with Hector SLAM is tested in several scenarios to evaluate the performance of the system, in terms of continuity and accuracy of the position estimation, and compares it with similar systems. The scenarios where the system is tested include reducing the laser sensor readings (low sampling rate), dynamic environments (change in the location of the obstacles), and the kidnapped robot situation. The results show that the system provides a significantly better accuracy and continuity of the position estimation in all scenarios, even in comparison with similar hybrid systems, except where there is a high and constant noise, where the performance of the hybrid filter and the simple PF seems almost the same.

Omnidirectional Vision Based Mobile Robot Hierarchical SLAM

2011 ◽  
Vol 464 ◽  
pp. 95-98
Author(s):  
Mao Hai Li ◽  
Li Ning Sun ◽  
Ming Qiang Pan
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Position Estimation ◽  
Medium Size ◽  
Accurate Estimation ◽  
Omnidirectional Vision ◽  
Alignment Algorithm ◽  
Feature Maps ◽  
Topological Graph ◽  
Local Map

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps of large environments is proposed. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF). The particle filter is used to extend the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through Kalman filter. Omnidirectional vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-Tree. Experimental results on real robot in a medium size, real indoor environment show the practicality and efficiency of our proposed method.

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