Construction of visual codebook to speed up visual-based simultaneous localization and mapping. Issue 1 (1st January 2017) - Sasaki, Hayato; Hamatsu, Fumiya; Hamagami, Tomoki
Abstract: We propose a visual-based self-localization system that requires less computational time and memory resources when utilized in an indoor environment. In this study, localization is accomplished by comparing an observation with a significant amount of preprocessed images. The larger the database, the slower the self-location calculation. We constructed an economical database (codebook for local image features), in terms of computational time and memory usage, to resolve this challenge. We sequentially registered new codes to avoid duplication. Additionally, we indexed the codebook to speed up the calculation when using an approximating search. The experiment result illustrated that the strategies of reducing the size of the codebook and approximating the calculation contributed to reducing the calculation cost and improving the self-localization accuracy.