Maximilian Hoh

2 accepted papers

2024

Probabilistic Closed-Loop Active Grasping

Henry Schaub, Christian Wolff, Maximilian Hoh, Alfred Schöttl

RA-L 2024

Picking a specific object is an essential task of assistive robotics. While the majority of grasp detection approaches focus on grasp synthesis from a single depth image or point cloud, this approach is often not viable in an unstructured, uncontrolled environment. Due to occlusion, heavy influence

Cited by 1 Source ↗Scholar ↗

2022

Probabilistic Fusion of Depth Maps With a Reliable Estimation of the Local Reconstruction Quality

Henry Schaub, Alfred Schöttl, Maximilian Hoh

RA-L 2022

Depth sensors in general, but especially consumer level sensors, have noise characteristics that are difficult to generalize due to external factors such as material specific properties or varying lighting conditions. The quality of the reconstructed surface from fused depth data may therefore vary

Cited by 2 Source ↗Scholar ↗