Introduction - If you have any usage issues, please Google them yourself
Abstract—Probabilistic approaches have proved very successful
at addressing the basic problems of robot localization and
mapping and they have shown great promise on the combined
problem of simultaneous localization and mapping (SLAM). One
approach to SLAM assumes relatively sparse, relatively unambiguous
landmarks and builds a Kalman filter over landmark
positions. Other approaches assume dense sensor data which
individually are not very distinctive, such as those available from
a laser range finder. In earlier work, we presented an algorithm
called DP-SLAM, which provided a very accurate solution to the
latter case by efficiently maintaining a joint distribution over
robot maps and poses. The approach assumed an extremely
accurate laser range finder and a deterministic environment.
In this work we demonstrate an improved map representation
and laser penetration model, an improvement in the asymptotic
efficiency of the algorithm, and empirical results of loop closing
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