A Systematic and Efficient Approach for Data Association in Topological Maps for Mobile Robot using Wavelet Transformation

Data association is a process that matches a recent observation with known data set, which is used for the localization of mobile robots. Edges in topological maps have rich information which can be used for the data association. However, no systematic approach on using the edge data for data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by $2^7-2^8$ times, for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.