Accurate mobile robot position determination using unscented Kalman filter

Houshangi, N.; Azizi, F.

doi:10.1109/ccece.2005.1557061

Cited by 11 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are few versions for the UKF which they differ from each other based on whether to include the random noise into the calculations. Houshangi and Azizi have used the UKF to successfully integrate information from odometry and a single axis gyroscope (Houshangi, 2005(Houshangi, , 2006Azizi, 2004). The mobile robot used in their work is a Pioneer 2-Dxe from ActivMedia Robotics Corporation.…”

Section: Wwwintechopencommentioning

confidence: 99%

Mobile Robot Position Determination

Azizi¹,

Houshangi²

2011

Recent Advances in Mobile Robotics

View full text Add to dashboard Cite

Section: Wwwintechopencommentioning

confidence: 99%

Mobile Robot Position Determination

Azizi¹,

Houshangi²

2011

Recent Advances in Mobile Robotics

View full text Add to dashboard Cite

“…Study on localization, which is an important technique in order to achieve the purposes of a mobile robot, has become popular [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. There are two methods of localization [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The relative positioning method uses an inertial measurement unit (IMU), which consists of a triaxis accelerometer and a triaxis gyroscope, or encoders attached to the mobile robot's wheels [1][2][3][4][5][6][7]. The inertial data from the IMU are accumulated in order to estimate position, velocity, and attitude.…”

Section: Introductionmentioning

confidence: 99%

Indoor Dead Reckoning Localization Using Ultrasonic Anemometer with IMU

Seo

Baek

2017

Journal of Sensors

View full text Add to dashboard Cite

Dead reckoning is an important aspect of estimating the instantaneous position of a mobile robot. An inertial measurement unit (IMU) is generally used for dead reckoning because it measures triaxis acceleration and triaxis angular velocities in order to estimate the position of the mobile robot. Positioning with inertial data is reasonable for a short period of time. However, the velocity, position, and attitude errors increase over time. Much research has been conducted in ways to reduce these errors. To position a mobile robot, an absolute positioning method can be combined with dead reckoning. The performance of a combined positioning method can be improved based on improvement in dead reckoning. In this paper, an ultrasonic anemometer is used to improve the performance of dead reckoning when indoors. A new approach to the equation of an ultrasonic anemometer is proposed. The ultrasonic anemometer prevents divergence of the mobile robot’s velocity. To position a mobile robot indoors, the ultrasonic anemometer measures the relative movement of air while the robot moves through static air. Velocity data from the ultrasonic anemometer and the acceleration and angular velocity data from the IMU are combined via Kalman filter. Finally we show that the proposed method has the performance with a positioning method using encoders on a good floor condition.

show abstract

“…Every subsequent position can be estimated by the relative movement of the robot. The most popular approach for position tracking is the Kalman Filter [1], [2]. The problem is referred to as local localisation.…”

Section: Introductionmentioning

confidence: 99%

Robust localisation for mobile robots in indoor environments

Hanses

Lüder

2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

View full text Add to dashboard Cite

In order to operate reliably in industrial environments, a mobile robot must know where it is. As part of a student project, a method has been developed for robust localisation in a partly ambiguous environment using a particle filter. This enables accurate estimations of the robot's position by merging odometry readings with measurements provided by a 240 degree laser rangefinder. The developed algorithm is able to solve the kidnapped robot problem in realtime with feasible computational effort. Even though the presented approach is customized for the given setup it can be easily adapted to perform in other environments.

show abstract

Accurate mobile robot position determination using unscented Kalman filter

Cited by 11 publications

References 10 publications

Mobile Robot Position Determination

Mobile Robot Position Determination

Indoor Dead Reckoning Localization Using Ultrasonic Anemometer with IMU

Robust localisation for mobile robots in indoor environments

Contact Info

Product

Resources

About