Computationally effective stereovision SLAM

Lazaros Nalpantidis; A. Gasteratos; G.C. Sirakoulis; A. Carbone

doi:10.1109/IST.2010.5548507

Computationally effective stereovision SLAM

Lazaros Nalpantidis, A. Gasteratos, G.C. Sirakoulis, A. Carbone

Research output: Contribution to book/anthology/report/conference proceeding › Book chapter › Research › peer-review

4 Citations (Scopus)

Abstract

In this paper a visual Simultaneous Localization and Mapping (SLAM) algorithm suitable for indoor area measurement applications is proposed. The algorithm is focused on computational effectiveness. The only sensor used is a stereo camera placed onboard a moving robot. The algorithm processes the acquired images calculating the depth of the scenery, detecting occupied areas and progressively building a map of the environment. The stereo vision-based SLAM algorithm embodies a custom-tailored stereo correspondence algorithm, the robust scale and rotation invariant feature detection and matching Speeded Up Robust Features (SURF) method, a computationally effective v-disparity image calculation scheme, a novel map-merging module, as well as a sophisticated Cellular Automata (CA)-based enhancement stage. The proposed algorithm is suitable for autonomously mapping and measuring indoor areas using robots. The algorithm is presented and experimental results for self-captured image sets are provided and analyzed.

Original language	English
Title of host publication	2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings
Number of pages	6
Publication date	1 Jan 2010
Pages	458-463
DOIs	https://doi.org/10.1109/IST.2010.5548507
Publication status	Published - 1 Jan 2010

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Computationally effective stereovision SLAM

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