Optimized parametric calibration of autonomous vehicles

摘要

Odometry, also referred to as dead reckoning, is one of the least expensive and most widely used methods for mobile robot localization. However, mobile robots implementing dead reckoning are plagued with inaccuracy caused by systematic and non-systematic errors. In many cases, the most dominant source of inaccuracy is systematic errors. Systematic errors are caused by differences between the nominal and the actual dimensions of vehicle parameters (such as wheel radius and wheelbase measurements). Because systematic errors are inherent to the vehicle, the dead reckoning inaccuracy grows unbounded. Fortunately, it is possible to largely eliminate systematic errors by calibrating the parameters such that the differences between the nominal dimensions and the actual dimensions are minimized. This work presents a method for calibration of mobile robot parameters using an optimization engine. A cost fluiction is developed based on the UMBmark (University of Michigan Benchmark) test pattern. This method is presented as a simple time efficient calibration tool for use during startup procedures of a differentially driven mobile robot. Comparisons are made between this method and an analytical calibration method developed at the University of Michigan. Results show that this tool consistently gives greater than 50% improvement in overall dead reckoning accuracy on an outdoor mobile robot, with respect to itself prior to calibration.