A SLAM Model Based on Omni-Directional Vision and Odometer

Abstract

A new SLAM measurement model based on omni-directional vision and odometer is proposed in this paper. A virtual stereo vision composed of an omni-directional vision sensor and an odometer. Scale Invariant Feature Transform is used to extract stable and available vision features from the omni-images. The 3-D locations of these features are initialized by the pixel coordinates and the odometer data by stereo projection, and the locations will be corrected during the SLAM process when they are observed again. It is demonstrated that the new model can make a good accuracy with FastSLAM algorithm, and the accuracy is greatly improved corresponding to the classical vision sensor.

Stereo visual SLAM system in underwater environment

“…In our experiment, 3-D points are computed using the equations (2), (3), (4) and (5). These points are the nature landmarks.…”

“…Therefore, feature detection and matching is the important process in the visual system. several papers consider the feature-based SLAM using camera images [5][6][7]. One herein must consider features that are as much invariant as possible with respect to any image transformation, when several unknown changes occur in the image, Point features are salient in images, have good invariant properties, and can be extracted with much less computation.…”