Mobile Robot Simultaneous Localization And Mapping In Large Area

Computational efficiency has become quite crucial while SLAM technology becomes much more mature. Recent research concerning information sparsification for Simultaneous Localization and Mapping (SLAM) has become quite popular. By classifying landmarks into different types, various Bayes topological network structures are built which could make information matrix exactly sparse. However, although the accuracy remains, the efficiency of SLAM algorithm is ruined during landmark classification. Compared to the research work shown before, our new algorithm which is based on Eustice's work is much more efficient and practical while maintaining high accuracy.Focusing on sparsing information matrix, geometrical meaning of Extended Information Filter SLAM (EIF-SLAM) and related formula are analyzed respectively. The conclusion is drawn that normalized information is sparse. Moreover the structure of information matrix in EIF-SLAM is put forward for the first time. According to the structure of information matrix, an improved algorithm based on EIF is introduced. Owning to the high efficiency of the special rules in sparse operation, the new algorithm solves SLAM very efficient by sparsing information matrix directly. The errors that come from sparsification decrease apparently due to loop-closure. In a word, the new algorithm realizes efficiency with consistent estimate of SLAM. At last, the relationship between sparsification and SLAM accuracy is analyzed theoretically.The new algorithm is simulated in a large scale environment. Information matrix sparsification, algorithm efficiency, relocalization, error and covariance are analyzed respectively after setting up parameters of motion model and observation model. Indoor two-wheel robot with camera and outdoor four-wheel robot with laser are taken into account in experiments. The two experiments are of great difference. Experiment one accomplishes by Frontier-II robot which is developed by our lab. Pictures represent landmarks that are associated by Scale Invariant Feature Transform (SIFT). Experiment two adopts standard Car Park Dataset which is famous in SLAM field. The environment of experiment two is outdoor car park and the robot is a four-wheel mobile car mounted with laser sensor. Cylindrical objects represent landmarks that are associated by Individual Compatibility Nearest Neighbor (ICNN). The results of simulation in large scale environment as well as indoor and outdoor experiments testify validity of the algorithm.