The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three‐dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstructured indoor environments. A key challenge in this scenario is that the robot moves in 6D and no odometry information is available. An extended information filter (EIF) is used to estimate the state vector containing the sequence of camera poses and some selected 3D point features in the environment. Data association is performed using a combination of scale invariant feature transformation (SIFT) feature detection and matching, random sampling consensus (RANSAC), and least square 3D point sets fitting. Experimental results are provided to demonstrate the effectiveness of the techniques developed.
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Ellekilde et al. (2007) studied this question.
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