Research on compressed EKF based SLAM algorithm for unmanned underwater vehicle

Abstract

The Extend Kalman Filter based algorithm for simultaneous localization and mapping cannot satisfy the requirement of real time map updating because of the increasing number of landmarks and the heavy calculating cost while AUV working for long time endurance. The Compressed EKF based SLAM is introduced in this paper. And the method of map management and the local map switch strategy are addressed, which divide the AUV navigating area into several local sub-maps. The navigation error calculating based on landmarks in sub-map is completed in local area by using Extend Kalman filter, and the global map updating is done only when the condition satisfied the switch rule of the sub-map. Finally the CEKF-SLAM based navigating method is tested with the trial data, and by comparing with the dead reckoning navigating result, the test results show that the navigation error of CEKF-SLAM algorithm is less than that of dead reckoning algorithm, and on the same time, the former reduces the calculation cost for AUV navigation.