Obstacle Detection Using a Voxel Octree Representation

This paper proposes a novel clustering algorithm based on an octree representation, suitable for stereovision-based driving assistance applications. The voxel octree representation takes into account the stereovision perspective and uncertainty model, thus dealing better with the 3D raw measurements. Given the internal partitioning of the octree, the indexing of children nodes can be used directly to determine 3D adjacent leaves. Therefore, the proposed clustering scheme exploits the octree structure to establish the 3D connectivity of voxels and detect 3D obstacle primitives. Computational complexity of clustering is lowered this way. These primitives are then merged based on adjacency criteria in order to obtain the 3D obstacles. KITTI's dataset was used to evaluate the accuracy of the approach. The proposed algorithm is applicable to k-ary trees for any dimensionality.