Obstacle Detection Using a Voxel Octree Representation This paper proposes a novel clustering algorithm based on an octree representation, suitable for stereovisionbased 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. KITTIxe2x80x99s dataset was used to evaluate the accuracy of the approach. The proposed algorithm is applicable to kary trees for any dimensionality.

Stair Feature Recognition and Modeling based on Binocular Vision Autonomous obstacle crossing for mobile robots is an important area of research. The premise of autonomous obstacle crossing is the ability of a robot to sense the environment and determine the size of obstacles. This paper uses stairs as the research object, which is the most common and complex obstacle model in structured environments. By analyzing the structural features of stairs, a method to extract the geometric features of stairs from a point cloud is proposed. From the geometric dimensions of the stairs, the data related to robot obstacle crossing are extracted to construct an obstacle model. The experimental results show that this method can effectively extract the characteristics of stairs and provide an obstacle model that can meet the requirements for obstacle crossing.