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- W51201421 abstract "Recently, the problem of learning volumetric maps from three-dimenisional range data has become quite popular in the context of mobile robotics. One of the key challenges in this context is to reduce the overall amount of data. The smaller the namber of data points, however, the fewer information is available to register the scans and to conputer a consistent map. In this paper we present a novel approach that estimates global constaints from the data and utilizes these contraints to improve the registration process. In our current system we simultaneously minimize the distance between scans and the distance of edges from planes extracted from the edges to obtain highly accurate three-dimensional modele of the environment. Several experiments carried out in simulation as well as with three-dimensional data obtained with a mobile robot in an outdoor environment we show that our approach yields seriously more accurate models compared to a standard apporach that does not utilize the global constraints." @default.
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- W51201421 date "2005-07-09" @default.
- W51201421 modified "2023-09-24" @default.
- W51201421 title "Improving simultaneous mapping and localization in 3D using global constraints" @default.
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