scholarly journals Camera Space Particle Filter for the Robust and Precise Indoor Localization of a Wheelchair

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Raul Chavez-Romero ◽  
Antonio Cardenas ◽  
Mauro Maya ◽  
Alejandra Sanchez ◽  
Davide Piovesan
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Theoretical Development ◽  
Comparative Performance ◽  
Vision Sensors ◽  
Experimental Implementation ◽  
Visual Markers ◽  
Order Of Magnitude ◽  
Observation Function ◽  
Robotic Wheelchair

This paper presents the theoretical development and experimental implementation of a sensing technique for the robust and precise localization of a robotic wheelchair. Estimates of the vehicle’s position and orientation are obtained, based on camera observations of visual markers located at discrete positions within the environment. A novel implementation of a particle filter on camera sensor space (Camera-Space Particle Filter) is used to combine visual observations with sensed wheel rotations mapped onto a camera space through an observation function. The camera space particle filter fuses the odometry and vision sensors information within camera space, resulting in a precise update of the wheelchair’s pose. Using this approach, an inexpensive implementation on an electric wheelchair is presented. Experimental results within three structured scenarios and comparative performance using an Extended Kalman Filter (EKF) and Camera-Space Particle Filter (CSPF) implementations are discussed. The CSPF was found to be more precise in the pose of the wheelchair than the EKF since the former does not require the assumption of a linear system affected by zero-mean Gaussian noise. Furthermore, the time for computational processing for both implementations is of the same order of magnitude.

Chaos control of permanent magnet synchronous motor using simple controllers

2018 ◽  
Vol 41 (8) ◽  
pp. 2352-2364 ◽  
Author(s):  
Arif Iqbal ◽  
Girish Kumar Singh
Keyword(s):  
Permanent Magnet ◽  
Chaos Control ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Industrial Applications ◽  
Stable Operation ◽  
Comparative Performance ◽  
Experimental Implementation ◽  
Chaotic Characteristic ◽  
The Stability

Owing to the superior properties and stable operation, the Permanent Magnet Synchronous Motor (PMSM) is preferably used in wide industrial applications. But, the stability of motor is found to be dependent on its initial operating condition, showing the chaotic characteristic. Therefore, this paper addresses the chaos control of PMSM by developing four simple but effective controllers, which are mathematically designed by using the principle of Lyapunov’s method for asymptotic global stability. A comparative performance assessment has been carried out for the developed controllers in terms of settling time and peak over shoot. Furthermore, the concept of conventional proportional-integration type controller has been extended to develop two more controllers for chaos control of PMSM. Numerical simulation has been carried out in Matlab environment for performance evaluation of developed controllers. The obtained analytical results have been validated through experimental implementation in real time environment on Multisim/Ultiboard platform.

Human-following robot using the particle filter in ISpace with distributed vision sensors

2006 ◽  
Vol 10 (2) ◽  
pp. 96-101 ◽  
Author(s):  
TaeSeok Jin ◽  
Kazuyuki Morioka ◽  
Hideki Hashimoto
Keyword(s):  
Particle Filter ◽  
Vision Sensors ◽  
Distributed Vision

scholarly journals An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
He Huang ◽  
Wei Li ◽  
De An Luo ◽  
Dong Wei Qiu ◽  
Yang Gao
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Iteration Process ◽  
Indoor Positioning ◽  
Matching Theory ◽  
Positioning Error ◽  
Matching Algorithm ◽  
Point Set ◽  
Particle Filter Algorithm ◽  
Error Constraint

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

scholarly journals Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xinlong Jiang ◽  
Yiqiang Chen ◽  
Junfa Liu ◽  
Dingjun Liu ◽  
Yang Gu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Indoor Localization ◽  
High Accuracy ◽  
Motion Sensors ◽  
Fusion Model ◽  
Indoor Location ◽  
Smooth Tracking ◽  
Error Accumulation ◽  
Learning Machine

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.

scholarly journals A Multi-User Personal Indoor Localization System Employing Graph-Based Optimization

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 157 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Historical Data ◽  
Dead Reckoning ◽  
Office Building ◽  
Localization System ◽  
Multiple Users ◽  
User Scenario ◽  
Single User

Personal indoor localization with smartphones is a well-researched area, with a number of approaches solving the problem separately for individual users. Most commonly, a particle filter is used to fuse information from dead reckoning and WiFi or Bluetooth adapters to provide an accurate location of the person holding a smartphone. Unfortunately, the existing solutions largely ignore the gains that emerge when a single localization system estimates locations of multiple users in the same environment. Approaches based on filtration maintain only estimates of the current poses of the users, marginalizing the historical data. Therefore, it is difficult to fuse data from multiple individual trajectories that are usually not perfectly synchronized in time. We propose a system that fuses the information from WiFi and dead reckoning employing the graph-based optimization, which is widely applied in robotics. The presented system can be used for localization of a single user, but the improvement is especially visible when this approach is extended to a multi-user scenario. The article presents a number of experiments performed with a smartphone inside an office building. These experiments demonstrate that graph-based optimization can be used as an efficient fusion mechanism to obtain accurate trajectory estimates both in the case of a single user and in a multi-user indoor localization system. The code of our system together with recorded dataset will be made available when the paper gets published.

Sign in / Sign up

Export Citation Format

Share Document