Toward Deployment of Large Scale Simultaneous Localisation and Map Building (SLAM) Systems
This paper describes recent progress toward the development and deployment of large-scale simultaneous localisation and map building (SLAM) systems. Two main theoretical results are presented. The first describes a closed form solution to the SLAM problem. This solution explains the many experimental and theoretical results obtained so far in the study of the SLAM problem and allows a precise means of analysing the performance of large scale, efficient, SLAM algorithms. The second result describes how best to choose landmarks to best maintain the performance of a large scale SLAM systems, and consequently which sets of landmarks may be deleted from a SLAM algorithm without compromising overall performance. This is the key to large-scale SLAM problems as it provides an efficient and systematic means of reducing the computational complexity of the problem with minimal loss of optimality. We conclude by describing an experimental program aimed at verifying and testing these SLAM algorithms in particular applications.
KeywordsExtend Kalman Filter Vehicle Location Vehicle Path Process Noise Covariance Specific Landmark
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