@inproceedings{Lin2021-PCTMA-NET, author = {Lin, Jianjie and Rickert, Markus and Perzylo, Alexander and Knoll, Alois}, title = {PCTMA-Net: Point Cloud Transformer with Morphing Atlas-based Point Generation Network for Dense Point Cloud Completion}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2021}, month = sep, address = {Prague, Czech Republic}, abstract = {Inferring a complete 3D geometry given an incomplete point cloud is essential in many vision and robotics applications. Previous work mainly relies on a global feature extracted by a Multi-layer Perceptron (MLP) for predicting the shape geometry. This suffers from a loss of structural details, as its point generator fails to capture the detailed topology and structure of point clouds using only the global features. The irregular nature of point clouds makes this task more challenging. This paper presents a novel method for shape completion to address this problem. The Transformer structure is currently a standard approach for natural language processing tasks and its inherent nature of permutation invariance makes it well suited for learning point clouds. Furthermore, the Transformer's attention mechanism can effectively capture the local context within a point cloud and efficiently exploit its incomplete local structure details. A morphing-atlas-based point generation network further fully utilizes the extracted point Transformer feature to predict the missing region using charts defined on the shape. Shape completion is achieved via the concatenation of all predicting charts on the surface. Extensive experiments on the Completion3D and KITTI data sets demonstrate that the proposed PCTMA-Net outperforms the state-of-the-art shape completion approaches and has a 10% relative improvement over the next best-performing method.}, keywords = {robotics}, } @inproceedings{Lin2020a, author = {Lin, Jianjie and Rickert, Markus and Knoll, Alois}, title = {{6D} Pose Estimation for Flexible Production with Small Lot Sizes based on {CAD} Models using {G}aussian Process Implicit Surfaces}, booktitle = {Proceedings of the {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, year = {2020}, month = oct, address = {Las Vegas, NV, USA}, abstract = {We propose a surface-to-surface (S2S) point registration algorithm by exploiting the Gaussian Process Implicit Surfaces for partially overlapping 3D surfaces to estimate the 6D pose transformation. Unlike traditional approaches, that separate the corresponding search and update steps in the inner loop, we formulate the point registration as a nonlinear non-constraints optimization problem which does not explicitly use any corresponding points between two point sets. According to the implicit function theorem, we form one point set as a Gaussian Process Implicit Surfaces utilizing the signed distance function, which implicitly creates three manifolds. Points on the same manifold share the same function value, indicated as \{1, 0, -1\}. The problem is thus converted into finding a rigid transformation that minimizes the inherent function value. This can be solved by using a Gauss-Newton (GN) or Levenberg-Marquardt (LM) solver. In the case of a partially overlapping 3D surface, the Fast Point Feature Histogram (FPFH) algorithm is applied to both point sets and a Principal Component Analysis (PCA) is performed on the result. Based on this, the initial transformation can then be computed. We conduct experiments on multiple point sets to evaluate the effectiveness of our proposed approach against existing state-of-the-art methods.}, keywords = {robotics}, } @inproceedings{Lin2018a, author = {Lin, Jianjie and Somani, Nikhil and Hu, Biao and Rickert, Markus and Knoll, Alois}, title = {An Efficient and Time-Optimal Trajectory Generation Approach for Waypoints under Kinematic Constraints and Error Bounds}, booktitle = {Proceedings of the {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, year = {2018}, month = oct, address = {Madrid, Spain}, abstract = {This paper presents an approach to generate the time-optimal trajectory for a robot manipulator under certain kinematic constraints such as joint position, velocity, acceleration, and jerk limits. This problem of generating a trajectory that takes the minimum time to pass through specified waypoints is formulated as a nonlinear constraint optimization problem. Unlike prior approaches that model the motion of consecutive waypoints as a Cubic Spline, we model this motion with a seven-segment acceleration profile, as this trajectory results in a shorter overall motion time while staying within the bounds of the robot manipulator's constraints. The optimization bottleneck lies in the complexity that increases exponentially with the number of waypoints. To make the optimization scale well with the number of waypoints, we propose an approach that has linear complexity. This approach first divides all waypoints to consecutive batches, each with an overlap of two waypoints. The overlapping waypoints then act as a bridge to concatenate the optimization results of two consecutive batches. The whole trajectory is effectively optimized by successively optimizing every batch. We conduct experiments on practical scenarios and trajectories generated by motion planners to evaluate the effectiveness of our proposed approach over existing state-of-the-art approaches.}, doi = {10.1109/IROS.2018.8593577}, keywords = {robotics, trajectory generation}, }