The main contribution of this paper is a new simultaneous localization and mapping SLAM algorithm for building dense three-dimensional maps using information ac- quired 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 ?lter 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 ?tting. Experimental results are provided to demonstrate the effectiveness of the techniques developed.
Journal of Field Robotics, 2007, Vol 24, Issue 1-2, p. 71-89