Some enhanced algorithms for robot navigation by omnidirectional cameras
To localize a point in a 3D scene, a previous approach used two omnidirectional cameras and combine with the Sum of Absolute Difference (SAD) to search for similar points. The old method gives low performance on repetitive textures. So an improvement for the SAD is proposed in this work. In the ego-motion estimation task, to take advantage of omnidirectional cameras, a new approach is presented: first, the Kanade-Lucas-Tomasi (KLT) feature tracker is improved for building a set of feature points, then the RANSAC algorithm is used to find the best consensus set from this, movement parameters are estimated in the next step by Newton-Gauss algorithm, and the final result is filtered again by Kalman filter. The improved KLT is robust against cameras' large rotation angles. By combining the proposed methods, a robot is able to reconstruct a 3D structure at a simple level. Experiments are performed using simulators: Google Sketchup and Pov-ray. Results show the new approach outperforms previous ones on problems that have been pointed out.