Object Mapping from Disparity Map by Fast Clustering

3D object bounding box detection is one of the most important aspects of robot vision for autonomous navigation. In this work, we propose a stereo vision based methodology for the purpose. The work relies on disparity map. First of all, pixels with the same disparity in the continuous space form the components. Detected components are then filtered based on size and density criteria. Finally, the filtered components are combined based on adjacency, connectivity strength and depth proximity. Thus, 2D object proposals are obtained and mapped to 3D bounding boxes. A dataset has been prepared to test the methodology. Performance has been compared with another system developed by Computer Vision Lab at INHA University, Incheon, South Korea. It is observed that the detection capability of the proposed system is superior. Furthermore, the computational speed makes the work suitable for robotic applications such as SLAM.