Error-Driven Refinement of Multi-scale Gaussian Maps
msra(2011)
摘要
The accuracy of Grid-based maps can be enhanced by putting a Gaussian in every cell of the map. However, this solution works
poorly for coarse discretizations in multi-scale maps. This paper proposes a method to overcome the problem by allowing several
Gaussians per cell at coarse scales. We introduce a multi-scale approach to compute an error measure for each scale with respect
to the finer one. This measure constitutes the basis of an incremental refinement algorithm where the error is used to select
the cells in which the number of Gaussians should be increased. As a result, the accuracy of the map can be selectively enhanced
by making efficient use of computational resources. Moreover, the error measure can also be applied to compress a map by deleting
the finer scale clusters when the error in the coarse ones is low.
The approach is based on a recent clustering algorithm that models input data as Gaussians rather than points, as is the case
for conventional algorithms. In addition to mapping, this clustering paradigm makes it possible to perform map merging and
to represent feature hierarchies under a sound theoretical framework. Our approach has been validated with both real and simulated
3-D data.
更多查看译文
关键词
Mahalanobis Distance, Hausdorff Distance, Range Image, Coarse Scale, Representation Error
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要