Date of Original Version
Abstract or Table of Contents
Accomplishing simultaneous localization and mapping (SLAM) in very large city environments is a great challenge because of theoretical and practical issues on computational complexity, dynamic environment, representation and data association. In this paper, we describe practical algorithms for dealing with the representation issues. Feature based, grid-based and direct methods are integrated into the framework of the hierarchical object based representation. The sampling and correlation based range image matching algorithm is developed to tackle the problem arising from uncertain, sparse and featureless data in outdoor environments. Experimental results of a 800 meter x 600 meter neighborhood demonstrate the feasibility of city-sized SLAM.