Single Camera Stereo Using Structure from Motion

J. McBride, M. Snorrason, T. Goodsell, R. Eaton, and M. R. Stevens

Proceedings SPIE Defense & Security, vol 5804, Orlando, FL (April, 2005)

Mobile robot designers frequently look to computer vision to solve navigation, obstacle avoidance, and object detection problems such as those encountered in parking lot surveillance. Stereo reconstruction is a useful technique in this domain and can be done in two ways. The first requires a fixed stereo camera rig to provide two side-by-side images; the second uses a single camera in motion to provide the images. While stereo rigs can be accurately calibrated in advance, they rely on a fixed baseline distance between the two cameras. The advantage of a single-camera method is the flexibility to change the baseline distance to best match each scenario. This directly increases the robustness of the stereo algorithm and increases the effective range of the system. The challenge comes from accurately rectifying the images into an ideal stereo pair. Structure from motion (SFM) can be used to compute the camera motion between the two images, but its accuracy is limited and small errors can cause rectified images to be misaligned. We present a single-camera stereo system that incorporates a Levenberg-Marquardt minimization of rectification parameters to bring the rectified images into alignment.

Features selected in one image are shown as solid dots. Hollow dots show the tracked locations of those features in another image; the goal is to align the features.

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