机器人地图创建新算法，DP-SLAM源程序-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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