Improved Position Estimation for Mobile Robots on Rough Terrain Using Attitude Information

Abstract

Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. This technical report introduces a new fuzzy logic expert rule-based method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our fuzzy logic rule-based sensor fusion method.