Dynamic Calibration between a Mobile Robot and SLAM Device for Navigation In this paper, we propose a dynamic calibration between a mobile robot and a device using simultaneous localization and mapping (SLAM) technology, which we termed as the SLAM device, for a robot navigation system. The navigation framework assumes loose mounting of SLAM device for easy use and requires an online adjustment to remove localization errors. The online adjustment method dynamically corrects not only the calibration errors between the SLAM device and the part of the robot to which the device is attached but also the robot encoder errors by calibrating the whole body of the robot. The online adjustment assumes that the information of the external environment and shape information of the robot are consistent. In addition to the online adjustment, we also present an offline calibration between a robot and device. The offline calibration is motionbased and we clarify the most efficient method based on the number of degreesoffreedom of the robot movement. Our method can be easily used for various types of robots with sufficiently precise localization for navigation. In the experiments, we confirm the parameters obtained via two types of offline calibration based on the degree of freedom of robot movement. We also validate the effectiveness of the online adjustment method by plotting localized position errors during a robots intense movement. Finally, we demonstrate the navigation using a SLAM device.

Model Free Calibration of Wheeled Robots Using Gaussian Process Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras, etc., by providing appropriate relationships between the corresponding reference frames. For wheeled robots equipped with cameralidar along with wheel encoders, calibration entails learning the motion model of the sensor or the robot in terms of the data from the encoders and generally carried out before performing tasks such as simultaneous localization and mapping (SLAM). This work puts forward a novel Gaussian Processbased nonparametric approach for calibrating wheeled robots with arbitrary or unknown drive configurations. The procedure is more general as it learns the entire sensorrobot motion model in terms of odometry measurements. Different from existing nonparametric approaches, our method relies on measurements from the onboard sensors and hence does not require the ground truth information from external motion capture systems. Alternatively, we propose a computationally efficient approach that relies on the linear approximation of the sensor motion model. Finally, we perform experiments to calibrate robots with unmodelled effects to demonstrate the accuracy, usefulness, and flexibility of the proposed approach.