Tag Archives: Odometry

Correcting systematic and non-systematic errors in odometry

Bibiana Fari�a, Jonay Toledo, Leopoldo Acosta, Improving odometric sensor performance by real-time error processing and variable covariance, Mechatronics, Volume 98, 2024 DOI: 10.1016/j.mechatronics.2023.103123.

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods.

Improving orientation estimation in a mobile robot for doing better odometry

M.T. Sabet, H.R. Mohammadi Daniali, A.R. Fathi, E. Alizadeh, Experimental analysis of a low-cost dead reckoning navigation system for a land vehicle using a robust AHRS, Robotics and Autonomous Systems, Volume 95, 2017, Pages 37-51, DOI: 10.1016/j.robot.2017.05.010.

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue especially when the localization sensors such as GPS are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework an Extended Kalman Filter (EKF) is utilized for estimation of attitude, heading and gyroscope sensor bias using a low-cost MEMS inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study using the proposed attitude and heading reference system (AHRS) and an odometer sensor, a low-cost aided DR navigation system has been designed. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for a land vehicle. The results indicate that the roll, pitch and heading are estimated by mean value errors about 0.83%, 0.68% and 1.13%, respectively. Moreover, they indicate that a relative navigation error about 3% of the traveling distance can be achieved using the developed approach in during GPS outages.