Application of the Self-Organization Algorithm with the Trend Redundancy in Navigation and Mapping of the Vehicle

We propose the self-organization algorithm with the trend redundancy for developing the intelligent navigation and mapping system of vehicles. The proposed navigation and mapping system incorporates an intelligent component called the action result acceptor. Within the action result acceptor, the self-organization algorithm uses a set of selection criteria to build a predictive model of navigation errors in vehicles. Lidar systems and inertial navigation systems are used as the base measurement systems. In the absence of navigation information from other sensors, the built predictive model is used to correct the navigation error. At the end of data processing, the Kalman filter is used to obtain an optimal estimate of the navigation error.