4 research outputs found
Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration
Current point cloud registration methods are mainly based on local geometric
information and usually ignore the semantic information contained in the
scenes. In this paper, we treat the point cloud registration problem as a
semantic instance matching and registration task, and propose a deep semantic
graph matching method (DeepSGM) for large-scale outdoor point cloud
registration. Firstly, the semantic categorical labels of 3D points are
obtained using a semantic segmentation network. The adjacent points with the
same category labels are then clustered together using the Euclidean clustering
algorithm to obtain the semantic instances, which are represented by three
kinds of attributes including spatial location information, semantic
categorical information, and global geometric shape information. Secondly, the
semantic adjacency graph is constructed based on the spatial adjacency
relations of semantic instances. To fully explore the topological structures
between semantic instances in the same scene and across different scenes, the
spatial distribution features and the semantic categorical features are learned
with graph convolutional networks, and the global geometric shape features are
learned with a PointNet-like network. These three kinds of features are further
enhanced with the self-attention and cross-attention mechanisms. Thirdly, the
semantic instance matching is formulated as an optimal transport problem, and
solved through an optimal matching layer. Finally, the geometric transformation
matrix between two point clouds is first estimated by the SVD algorithm and
then refined by the ICP algorithm. Experimental results conducted on the KITTI
Odometry dataset demonstrate that the proposed method improves the registration
performance and outperforms various state-of-the-art methods.Comment: 12 pages, 6 figure
Robotic Assembly Using 3D and 2D Computer Vision
The content of this thesis concerns the development and evaluation of a robotic cell used for
automated assembly. The automated assembly is made possible by a combination of an eye-inhand
2D camera and a stationary 3D camera used to automatically detect objects. Computer
vision, kinematics and programming is the main topics of the thesis. Possible approaches to
object detection has been investigated and evaluated in terms of performance. The kinematic
relation between the cameras in the robotic cell and robotic manipulator movements has been
described. A functioning solution has been implemented in the robotic cell at the Department
of Production and Quality Engineering laboratory.
Theory with significant importance to the developed solution is presented. The methods used
to achieve each part of the solution is anchored in theory and presented with the decisions and guidelines made throughout the project work in order to achieve the final solution.
Each part of the system is presented with associated results. The combination of these results yields a solution which proves that the methods developed to achieve automated assembly works as intended. Limitations, challenges and future possibilities and improvements for the solution is then discussed.
The results from the experiments presented in this thesis demonstrates the performance of the
developed system. The system fulfills the specifications defined in the problem description and is functioning as intended considering the instrumentation used