Accurate relative localization using odometry

All mobile robots suffer from odometry error. Relative localization from odometry has both the systematic and the non-systematic errors. However, once a precise system error model and its parameters are given, the accuracy of odometry can be remarkably improved. Most previous works on this effort focused on the differential drive robots with little attention to the other types of mobile bases. In this paper, we analyze sources of odometry error and propose an error model for the synchro drive robot. We then describe a novel procedure to accurately estimate the error parameters of the derived error model and the covariance matrix of the synchro drive robot. However, this procedure is general for all mobile bases, so we also apply our method for the differential drive robots and show experiments. This new process uses the shape of the path, as opposed to just end points, to estimate the error parameters and covariance matrix. We happen to use the generalized Voronoi graph to generate this path. Experimental results validate the error model of the synchro drive robot and precise estimation ability of the proposed method for the synchro and the differential drive robots.

Authors

• Nakju Lett Doh
• Howie Choset
• Wan Kyun Chung

Keywords

• Mobile robots
• Covariance matrix
• Parameter estimation
• Robot sensing systems
• Wheels
• Mechanical engineering
• Biomechatronics
• Biosensors
• Mechanical sensors
• Technology planning
• Localization Accuracy
• Precise Estimates
• Error Model
• Systematic Errors
• Sources Of Error
• Error Parameters
• Error Covariance
• Error Covariance Matrix
• Precision Error
• Navigation
• General Method
• Position Error
• End Position
• Steepest Descent Method
• Estimated Covariance Matrix
• Web Map
• End Nodes
• General Covariance
• Left Wheel
• Endpoint Error
• Wheel Radius
• Moment Equations