Multi-Spectrum Superpixel Based Obstacle Detection Under Vegetation Environments

Robust obstacle detection is an important task for unmanned ground vehicle(UGV). Vegetation in off-road environments poses great challenges to this task. Usually, vegetation should not be considered as obstacles for off-road UGVs since they are soft and drivable. On the other hand, there are also possibilities that real obstacles exist in the vegetation, which makes the problem difficult. In this paper, a novel multi-spectrum data fusion based algorithm for partial occluded obstacle detection under complex vegetation environment is proposed. First a RGB and Near-infrared (NIR) multi-spectrum superpixel based segmentation strategy is employed to accurately segment the objects in the image. Obstacle candidate superpixels are then obtained through simple geometric computation in 3D laser data. Finally, the heterogeneous texture and 3D features are extracted from each candidate superpixel and fed in Support Vector Machine (SVM) to distinguish the real obstacles from vegetation. Experimental results on real data acquired from various vegetation environments demonstrate our success.