ABSTRACT
A new obstacle detection method using time-of-flight 3D imaging sensor based on range clusters is proposed. To effectively reduce the influence of outlier and noise in range images, we utilize intensity images to estimate noise deviation of the range images and a weighted local linear smoothing is used to project the data into a new manifold surface. The proposed method divides the 3D imaging data into range clusters with different shapes and sizes according to the distance ambient relation between the pixels, and some regulation criterions are set to adjust the range clusters into optimal shape and size. Experiments on the SwissRanger sensor data show that, compared to the traditional obstacle detection methods based on regular data patches, the proposed method can give more precious detection results.
