Design of Kalman filters for mobile robots; evaluation of the kinematic and odometric approach

Larsen, Thomas Dall; Hansen, Kristoffer Lindskov; Andersen, Nils Axel; Ravn, Ole

doi:10.1109/cca.1999.801027

Cited by 60 publications

(31 citation statements)

References 7 publications

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“…9(b). As shown in this figure, the second z-axis position was not satisfied with the failure detection criteria (16). Except for the distance data between the second transmitter and the front receiver, the measurement equation was re-composed of the unblocked distance data.…”

Section: Performance Evaluation Of the Pus/dr Integratedmentioning

confidence: 99%

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The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

Kim

Yoon

Lee³

et al. 2011

Int. J. Control Autom. Syst.

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In this paper, an integration system is proposed to improve the positioning performance of a mobile robot by fusing a Pseudolite Ultrasonic System (PUS), an absolute position measurement system using direct ultrasonic waves, with a Dead Reckoning (DR) odometer. As an integration algorithm of the absolute position measurement system and DR, two methods are proposed. In the loosely coupled method, the PUS and the DR calculate the position independently and a Kalman filter estimates the position using position information from the PUS and the DR. In the tightly coupled method, the PUS provides the distance between the ultrasonic transmitters and receivers without calculating the position directly and the DR provides the translational and rotational displacement of the mobile robot. The Kalman filter then estimates the position using information from the PUS and the DR. In addition, to improve the positioning performance in case the line-of-sight (LOS) between the ultrasonic transmitter and receiver is blocked due to obstacles, a positioning failure detection algorithm and reckoning methods are proposed. The positioning performances of the proposed PUS/DR integrated systems and the validity of the positioning failure detection algorithm are verified and evaluated by experiments.

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Section: Performance Evaluation Of the Pus/dr Integratedmentioning

confidence: 99%

“…Next, the kinematic state equation of the mobile robot in two dimensions can be described as follows [15,16]:…”

Section: The Localization Of a Mobile Robot Using The Pus/dr Integratmentioning

confidence: 99%

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

Kim

Yoon

Lee³

et al. 2011

Int. J. Control Autom. Syst.

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show abstract

“…However, the small microcontroller, the limited memory (1 kB RAM) and considerable electronic noise make realtime signal processing very problematic. For example, it is impossible to store several periods of the whole signal to apply recursive signal analysis approaches [26] or a Kalman filter [21]. In this work, we focus on Constraint Satisfaction Problem (CSP)-based algorithms for performing real-time odometric signal processing to allow accurate detection of each wheel rotation.…”

Section: Introductionmentioning

confidence: 99%

Encoder-free odometric system for autonomous microrobots

Kernbach

2012

Mechatronics

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“…In the algorithm proposed here, each axle module has the potential to fuse data from wheel encoders, rate sensors, accelerometers, and GPS using a kinematic model based Kalman filter as in [16]. In our case, the axle modules are initially assumed to be operating with two optical encoders, one for each wheel, and the flexible frame module will be instrumented with strain gauges.…”

Section: Sensor Fusionmentioning

confidence: 99%

Internal posture sensing for a flexible frame modular mobile robot

Merrell

Minor²

2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)

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-A sensor fusion algorithm for flexible framed modular mobile robots is presented in this paper. This algorithm uses traditional Kalman filters and rigid axle kinematic models to predict the global posture of each axle. A Covariance Intersection filter is then proposed for fusing these axle modules using data provided by the compliant frame module.Modeling and instrumentation of the compliant frame module is the remaining focus. The instrumented frame is required to estimate the relative posture of the axles as well as the force components and moment that the beam exerts on each axle. These estimates must also be valid for large deflections in order to accommodate a reasonable turning radius. To accomplish these goals the beam equations are derived. A linear interpolation of the strain gauge data is used to calculate posture, force, and moment estimates. Experimental results show that the frame module can yield accurate relative posture estimates for large deflection.

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Design of Kalman filters for mobile robots; evaluation of the kinematic and odometric approach

Cited by 60 publications

References 7 publications

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

Encoder-free odometric system for autonomous microrobots

Internal posture sensing for a flexible frame modular mobile robot

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