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Dense 3 D Mapping with Monocular Vision : Bridging the Gap Between Robotics and Computer Vision

Currently, the vast majority of autonomous mapping in robotics relies on direct measurements from costly devices such as sonar, infrared, and laser range finders. Adapting established methodologies from the structure−from−motion (SFM) subfield of computer vision to data commonly available in robotics, we have created a unique toolkit, able to render visually dense 3d maps from odometrically annotated monocular vision. Though computationally intensive, monocular vision provides a low−cost, yet highly capable alternative to currently popular mapping sensors. Future research on application of our work for navigational uses hopes to create inexpensive monocular robotic systems capable of autonomous navigation.