Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles

An enhanced odometry technique based on the heading sensor called “clino-gyro” that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. In the proposed scheme, inclinometer data are used to compensate for the gyro drift due to roll/pitch perturbation of the vehicle while moving on the rough terrain. Providing independent information about the rotation (yaw) of the vehicle, clino-gyro is used to correct differential odometry adversely affected by the wheel slippage. Position estimation using this technique can be improved significantly, however for the long term applications it still suffers from the drifts of the gyro and translational components of wheel skidding. Fusing this enhanced odometry with the data from environmental sensors (sonars, laser range finder) through Kalman filter-type procedure a reliable positioning can be obtained. This technique has been implemented on-board of an experimental skid-steered vehicle. Obtained precision is sufficient for navigation in underground mining drifts. For open-pit mining applications further improvements can be obtained by fusing proposed localization algorithm with GPS data