Authors:
Blake Troutman
and
Mihran Tuceryan
Affiliation:
Department of Computer and Information Science, Indiana University - Purdue University Indianapolis, Indianapolis, U.S.A.
Keyword(s):
Simultaneous Localization and Mapping (SLAM), Structure From Motion, Map Initialization, Model Selection, Monocular Vision, Stereo Vision.
Abstract:
Simultaneous localization and mapping (SLAM) is a widely adopted approach for estimating the pose of a sensor with 6 degrees of freedom. SLAM works by using sensor measurements to initialize and build a virtual map of the environment, while simultaneously matching succeeding sensor measurements to entries in the map to perform robust pose estimation of the sensor on each measurement cycle. Markerless, single-camera systems that utilize SLAM usually involve initializing the map by applying one of a few structure-from-motion approaches to two frames taken by the system at different points in time. However, knowing when the feature matches between two frames will yield enough disparity, parallax, and/or structure for a good initialization to take place remains an open problem. To make this determination, we train a number of logistic regression models on summarized correspondence data for 927 stereo image pairs. Our results show that these models classify with significantly higher preci
sion than the current state-of-the-art approach in addition to remaining computationally inexpensive.
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