5 accepted papers
Motion detection in 3D LiDAR is crucial for autonomous systems. While deep learning dominates Moving Object Segmentation (MOS), the potential of learning-free approaches remains underexplored. Unlike problems like semantic segmentation, motion can be explicitly modeled, potentially enabling efficien
In this paper, we present an integrated, model-based system for state estimation and control in dynamic manipulation tasks with partial observability. We track a belief over the system state using a particle filter from which we extract a Gaussian Mixture Model (GMM). This compressed representation…
Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasp
In this paper, we present an approach for the probabilistic fusion of 3D sensor measurements. Our fusion algorithm is based on truncated signed distance functions. It explicitly considers the measurement noise by modeling the surface using random variables. Furthermore, our proposed surface model pr
Reliable precision grasping is a pre-condition for manipulation tasks e.g. in assembly and packaging tasks. Especially for small and light objects robust grasping is extremely challenging since even slight errors in the object pose or dimensions lead to irreparable failures caused by unintended fing…