An efficient approach to pose tracking based on odometric error modelling for mobile robots

Yang, Jun; Yang, Jianguo; Cai, Zixing

doi:10.1017/s0263574714000654

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…In all experiments, the robot had to follow some path, simulating real operation conditions. Based on the data collected during the trials, the velocities, position and orientation uncertainties were calculated based on equations (36), (37), (44) and (41).…”

Section: Resultsmentioning

confidence: 99%

“…A similar method is presented by Yang et al [41]. In addition to the model, Yang et al present a method for compensating slippage due to differences on the wheels radii (systematic error).…”

Section: B Development Of Accurate Robot Modelsmentioning

confidence: 99%

“…The uncertainties associated with the robot's velocities and pose were estimated based on the models proposed in this paper equations (36), (37), (44) and (41) and the data from the experiment.…”

Section: B Experimentsmentioning

confidence: 99%

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The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

et al. 2019

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This paper analyses the impact of parametric uncertainties of a mobile robot kinematic model on velocity and pose estimation, providing models and quantifiable knowledge about them. Most works neglect how the uncertainty regarding the robot's construction aspects (such as wheels radii, the distance between them, and the robot's center of mass) affects both velocities and pose estimation. To help readers understand the influence of such parametric uncertainties, we performed experiments in a simulator and used the collected data on the proposed models. The paper also analyses how the magnitude of velocities considered by the controllers and the followed path can decrease or increase the impact of the parametric uncertainties. The proposed models and presented analysis help understanding the influence (isolated or simultaneous) of different sources of uncertainty in the robot's velocities and pose estimation. This knowledge can be applied to estimate uncertainties for localization methods based on data fusion, complementing or even avoiding the experimental procedures. Also, the development of controllers, robot simulators and new methodologies for parameters' calibration and the design and construction of new robots can also profit from the results presented in this paper.

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Section: Resultsmentioning

confidence: 99%

Section: B Development Of Accurate Robot Modelsmentioning

confidence: 99%

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The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

et al. 2019

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“…Kalman filter is used to obtain the optimal data, so that the indoor robot positioning has better accuracy and adaptability [17,18]. In order to solve the problem that the positioning accuracy of mobile robots decreases sharply due to the sensor measurement error and the pose error caused by the robot model in the positioning process, a filtering algorithm is proposed [19,20]. Based on the standard Kalman filter, when the sensor measurement error exists, the positioning accuracy is improved by adjusting the size of the state covariance matrix to resist the filtering divergence caused by the pose error.…”

Section: Introductionmentioning

confidence: 99%

Robot indoor location modeling and simulation based on Kalman filtering

Lu¹,

Li²

2019

J Wireless Com Network

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Wireless signal fingerprint positioning technology has been widely used in indoor positioning. In view of the influence of a large number of interference noise in indoor, the error of receive signal strength indicator is large, the more complex and chaotic indoor environment, the location accuracy deviation of the system will be very large; an algorithm based on Kalman filter is proposed to filter the velocity and direction of motion of indoor robots. The position coordinates of the robot are estimated by RSSI-based positioning method, and the indoor robot positioning model and Kalman filter model are established. Kalman filter autoregressive algorithm is used to optimize the estimated position coordinates of the robot. Mathematical reasoning and simulation results show that the probability of positioning error is 80% when Kalman filter is not used, and the location error is controlled within 1.2 m after Kalman filter, which effectively improves the location accuracy of indoor robots.

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“…In addition, cumulative errors of robot pose usually result in overlapping and distorting maps. This effect is also known as the odometer measurement error problem (Yang, Yang, & Cai, 2015).…”

Section: Introductionmentioning

confidence: 99%

Improved grid mapping technology based on Rao-Blackwellized particle filters and the gradient descent algorithm

Zhang

Wang

Yuan

et al. 2019

Systems Science & Control Engineering

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Recently, the Rao-Blackwellized particle filter (RBPF) has been used to solve the problem of simultaneous localization and mapping (SLAM). Using the odometer information of robot to calculate the proposed distribution requires a number of sampled particles, which increases the calculation complexity in the RBPF operation. In this paper, we integrate the odometer measurement and sensor observation into the proposed distribution, effectively reducing the particle sample scale. To reduce the inconsistency in the map model caused by the cumulative error of the odometer information of robot, we applied a gradient descent algorithm to fuse the sensor data to obtain the real-time attitude angle. This combination method, based on the robot operation system (ROS), runs on a platform of self-built mobile robot equipped with a laser rangefinder. The experimental results show that this method can realize the online real-time high-precision grid map which provides a new approach for robot navigation and SLAM.

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An efficient approach to pose tracking based on odometric error modelling for mobile robots

Cited by 4 publications

References 16 publications

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

Robot indoor location modeling and simulation based on Kalman filtering

Improved grid mapping technology based on Rao-Blackwellized particle filters and the gradient descent algorithm

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