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... data collection, the robot suffered a final odometric error of 70 meters and 180 degrees. This dataset was first pre-filtered by a recently developed routine for self-calibrating of kinematic models =-=[14]-=-, then processed using the algorithm described in this paper. The final result is a accurate map. No experiments were performed using the raw dataset. More results can be found in [19]. 6 Related Work...

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...ublisher Item Identifier S 1042-296X(02)05282-5. they measured the effective axle length due to skid steering which differs from that given by the specifications for the robot. Finally, Roy and Thrun =-=[19]-=- suggested an algorithm that uses the robot’s sensors to automatically calibrate the robot as it operates. In a series of papers, Borenstein ([6] and references therein) also suggested a method to imp...

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...ecise control. Regarding odometry calibration, some previous work has focused on mobile robots calibrating their odometry models automatically based on their sensors. On wheeled robots, Roy and Thrun =-=[26]-=- calibrate the odometry using an incremental maximum likelihood method, while Martinelli et al. [27] and Larsen et al. [28] use an augmented Kalman filter to estimate odometry errors. On a legged robo...

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...body schema extension in this subsection. Automatic calibration of a model is addressed by some traditional methods from machine learning [84], system identification [85], [86], or probability theory =-=[87]-=-. More specifically, there is a number of solutions to the automated calibration of a kinematic chain [88]–[90] or a hand/eye setup [91]. Typically, a sampling period in which different configurations...

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... In the same spirit, in [6] there is a generalization to arbitrary trajectories and different kinematics. Another approach is called auto-calibration or SLAC (Simultaneous Localization And Calibration), in which calibration is part of the normal activity of the robot and happens without human intervention. A possibility is to use an Extended Kalman Filter (EKF) that estimates both the pose of the robot and the odometry parameters [7], [8], [9]. In this case, there are observability issues to take into account: this is considered in [10] for different combinations of sensors and kinematics. In [11] the authors propose a maximum-likelihood model-free approach that does not seek a physical explanation of the odometry errors. In [12], the authors use a maximum-likelihood method for estimating the odometry parameters of a differential-drive robot, using the absolute observations of an external camera. The method is particularly simple because the problem is completely linear, and therefore can be solved via linear least-squares. A more difficult problem is to simultaneously estimate the odometry parameters plus some other sensor parameters. A A. Censi is with the Control & Dynamical Systems...

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... we use is as follows: if z is a scalar that represents an error-prone measured value, then ˆz represents an estimate of the true value, and ˜z represents a distribution of uncertainty. Roy and Thrun =-=[6]-=- use online localization techniques to calibrate the parameters of a sub-Cartesian action model. A sub-Cartesian action model uses the change in wheel shaft encoder counts between time k − 1 and time ...