Structure-based object detection from scene point clouds

Abstract

We present a novel algorithm for object detection from the scene point clouds acquired in complex environments based on the structure analysis of objects. First, the scene point clouds are partitioned using the Gaussian map, and the primitive shapes are extracted from the segments. Second, each primitive shape is represented by a node, and the connection between two shapes is represented by an edge. The topological graph of the scene is reconstructed by defining the node properties, edge properties, and connection types, and the structure of the target objects is also analyzed. Then, an “assembly-matching” strategy is proposed to recognize the target objects in the scene. The qualified primitive shapes are assembled iteratively until no more suitable shapes are found. At the same time, the connection string of the combinational shapes is recorded using several numbers. Finally, the target object is detected by comparison to the connection string. The object is detected successfully if the structure coding between the iterative shapes is in line with the target object. The experimental results show that the proposed method can quickly detect common objects from massive point clouds.