Robot localisation and mapping using data fusion via integration of covariance intersection and interval analysis for a partially known map

The problem considered here is that of robot navigation, localisation, and mapping using an extended Kalman filter, interval analysis and covariance intersection for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are partially known. There are various approaches to the problem, but here focus is on an approach which can guarantee performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of position estimate of a mobile robot and to build the environmental map of the surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), such as accelerometers, gyroscopes, to measure acceleration and angle rate, while obstacle detection and map-making is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for the robots with low cost sensors by forming an intelligent sensor system. This is 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 each other's limitations, so that the resulting performance of the sensor system is better than of its individual components.