In a different approach, [PERSON] et al. [7] directly estimates the position and orientation of the mobile robot using the information provided by an onboard precise 2D LIDAR processed with simultaneous location and mapping (SLAM) [8]. In this case, the odometry was used as an initial estimation of the relative motion in order to improve the computational efficiency of SLAM. More recently, [PERSON] et al. [9] fused the information gathered from one IMU and two low-precision 2D LIDARs placed transversally to estimate the position and orientation of a mobile robot. However, the main disadvantage of using LIDARs is the cost of the sensor that is proportional to its measurement accuracy. In the specific case of omnidirectional mobile robots, the determination of the odometry from the velocity gathered from the wheels has similar error sources but more complexity because of having more degrees of freedom in the motion [10]. In this direction, [PERSON] et al. [11] proposed a method for the calibration of small three-wheeled omnidirectional mobile robots in order to reduce positioning errors. The procedure was based on the determination of two corrective indices for the inverse kinematic matrix used by the odometry to estimate the position of the robot. This method consists of: (1) determining the kinematic equations of the robot; (2) registering of the motion of the non-calibrated robot moving along a straight line; (3) evaluating the longitudinal error (\(x_{e}\)), lateral error (\(y_{e}\)) and angular error (\(\theta_{e}\)) between the target and real trajectory positions; and (4) the computation of some corrective indices. This method corrects the longitudinal (\(x_{e}\)) and lateral (\(y_{e}\)) errors separately. First, a lateral corrective matrix, which compensates the lateral position error of the robot (\(y_{e}\)), is computed from the angular error of the robot (\(\theta_{e}\)). Secondly, a longitudinal corrective factor used to eliminate the longitudinal position error (\(x_{e}\)) is computed from the longitudinal (\(x_{e}\)) and lateral errors (\(y_{e}\)). Finally, both indices are multiplied with the Jacobian matrix used by the odometry to estimate the velocities of the wheels. The corrected Jacobian matrix is then used to compute the corrected angular velocities of the wheels. This proposal was experimentally validated with different trajectories, comparing the positioning errors before and after calibration. Results showed significant improvements: the root mean square (RMS) of the positioning error was reduced between 68% and 91% in double-squared, double-triangle and circular paths. In this case, the analysis of the trajectories and positioning errors evidenced that the improvement depends on the type of trajectory being accurate in straight trajectories and less accurate in the case of combined straight and curved trajectories. Similarly, [PERSON] et al. [12] presented an odometry calibration method for medium-size three-wheeled omnidirectional mobile robots based on the correction of its kinematic model. The method consists on gathering discrete position and orientation data and estimating the kinematic parameters by a least square method. The information required by this process is the initial and final positions of the mobile robot through N experiments (multiple data sets). This proposal is not limited by the relationship between the parameters used in the kinematic equations, so the obtained kinematic model may better describe the odometry of the mobile robot. [PERSON] et al. (2012) verify their calibration method by comparing the ideal trajectory and the trajectories before and after calibration. In a similar direction, [PERSON] et al. (2013) presented a method for the reduction of positioning errors of four-wheel omnidirectional mobile robots using Mecanum wheels.