Recently various algorithms for building of three-dimensional maps of indoor environments have been proposed. In this work we use a Kinect camera that captures RGB images along with depth information for building three-dimensional dense maps of indoor environments. Commonly mapping systems consist of three components; that is, first, spatial alignment of consecutive data frames; second, detection of loop-closures, and finally, globally consistent alignment of the data sequence. It is known that three-dimensional point clouds are well suited for frame-to-frame alignment and for three-dimensional dense reconstruction without the use of valuable visual RGB information. A new fusion algorithm combining visual features and depth information for loop-closure detection followed by pose optimization to build global consistent maps is proposed. The performance of the proposed system in real indoor environments is presented and discussed.
DOI: https://doi.org/10.1117/12.2187929
ResearchGate: https://www.researchgate.net/publication/290858013_A_fusion_algorithm_for_building_three-dimensional_maps
Semantic Scholar: https://api.semanticscholar.org/CorpusID:124862218
Vokhmintsev, A. Makovetskii, V. Kober, I. Sochenkov, V. Kuznetsov. A fusion algorithm for building three-dimensional maps. Proc. SPIE's 60 Annual Meeting: Applications of Digital Image Processing XXXVIII, San Diego, California, United States. Vol. 9599, 2015. P.p. 29-36. Doi:10.1117/12.2187929