Abstract
Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.
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Lemaire, T., Berger, C., Jung, IK. et al. Vision-Based SLAM: Stereo and Monocular Approaches. Int J Comput Vision 74, 343–364 (2007). https://doi.org/10.1007/s11263-007-0042-3
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DOI: https://doi.org/10.1007/s11263-007-0042-3