Automated Object Pose Recognition by a Combination of Stereo Cameras and 2D Object Detection
Rostock, Univ., Master Thesis, 2018
The ability to visually recognize the 3D pose of objects would be helpful in many industrial applications. Existing approaches on this topic restrict the pose estimation to simplified scenarios, e.g. where a pose consists of just a 2D position and an angle around the vertical axis, or they require a priori knowledge. In this thesis methods are investigated to estimate a 6 DoF pose and 3D extends of arbitrary objects captured by a stereo camera. No knowledge about the shape of objects is required beforehand. A prototype implementation is provided. It first detects the objects in 2D employing a CNN trained for object detection. Then, the depth information is used to reconstruct a 3D point cloud and isolate the objects of interest. Finally, pose and size are estimated based on dense and sparse registration methods. At the end, the whole method is tested on artificially generated stereo images of fish. The results show remaining challenges especially regarding the robustness.