Computationally effective stereovision SLAM

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.