Robust localisation using data fusion via integration of covariance intersection and interval analysis

The problem considered here is that of robot navigation and localisation using an extended Kalman filter, interval analysis and covariance intersection. There are various approaches to the problem, but here focus is on an approach which can guarantee performance of sensor based navigation. The guaranteed performance is quantified by explicit bounds of position estimate of a mobile robot. The focus here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system which can provide mathematically provable performance guarantees that are achievable in practice. This can be accomplished by combining the sensors measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for one another’s limitations, so that the resulting performance of the sensor system is better than of its individual components.