Fusing laser reflectance and image data for terrain classification for small autonomous robots

摘要

Knowing the terrain is vital for small autonomous robots traversing unstructured outdoor environments. We present a technique using 3D laser point clouds combined with RGB camera images to classify terrain into four pre-defined classes: grass, sand, concrete, and metal. Our technique first segments the point cloud into distinct regions and then applies a simple classifier to determine the classification of each region. We demonstrate three classification and four segmentation algorithms on five outdoor environments. Classification and segmentation algorithms which use more information outperform information poor combinations.