166 citations · 328 across the 12 of their papers we have counts for
17 papers
MOTPose: Multi-object 6D Pose Estimation for Dynamic Video Sequences using Attention-based Temporal Fusion
Arul Selvam Periyasamy, Sven Behnke
Cluttered bin-picking environments are challenging for pose estimation models. Despite the impressive progress enabled by deep learning, single-view RGB pose estimation models perf…
Efficient Multi-Object Pose Estimation using Multi-Resolution Deformable Attention and Query Aggregation
Arul Selvam Periyasamy, Vladimir Tsaturyan, Sven Behnke
Object pose estimation is a long-standing problem in computer vision. Recently, attention-based vision transformer models have achieved state-of-the-art results in many computer vi…
YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation
Arul Selvam Periyasamy, Arash Amini, Vladimir Tsaturyan +1
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network…
Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels
Arul Selvam Periyasamy, Luis Denninger, Sven Behnke
Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing…
ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses
Arul Selvam Periyasamy, Catherine Capellen, Max Schwarz +1
Object pose estimation is a key perceptual capability in robotics. We propose a fully-convolutional extension of the PoseCNN method, which densely predicts object translations and…
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network…
T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D pose estimation is the task of predicting the translation and orientation of objects in a given input image, which is a crucial prerequisite for many robotics and augmented real…
SynPick: A Dataset for Dynamic Bin Picking Scene Understanding
Arul Selvam Periyasamy, Max Schwarz, Sven Behnke
We present SynPick, a synthetic dataset for dynamic scene understanding in bin-picking scenarios. In contrast to existing datasets, our dataset is both situated in a realistic indu…
Team NimbRo's UGV Solution for Autonomous Wall Building and Fire Fighting at MBZIRC 2020
Christian Lenz, Jan Quenzel, Arul Selvam Periyasamy +7
Autonomous robotic systems for various applications including transport, mobile manipulation, and disaster response are becoming more and more complex. Evaluating and analyzing suc…
Autonomous Wall Building with a UGV-UAV Team at MBZIRC 2020
Christian Lenz, Max Schwarz, Andre Rochow +4
Constructing large structures with robots is a challenging task with many potential applications that requires mobile manipulation capabilities. We present two systems for autonomo…
Refining 6D Object Pose Predictions using Abstract Render-and-Compare
Arul Selvam Periyasamy, Max Schwarz, Sven Behnke
Robotic systems often require precise scene analysis capabilities, especially in unstructured, cluttered situations, as occurring in human-made environments. While current deep-lea…
Remote Mobile Manipulation with the Centauro Robot: Full-body Telepresence and Autonomous Operator Assistance
Tobias Klamt, Max Schwarz, Christian Lenz +21
Solving mobile manipulation tasks in inaccessible and dangerous environments is an important application of robots to support humans. Example domains are construction and maintenan…
Autonomous Dual-Arm Manipulation of Familiar Objects
Dmytro Pavlichenko, Diego Rodriguez, Max Schwarz +3
Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios. However, this is a challenging undertaking, particularly in terms of perception a…
Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks
Arul Selvam Periyasamy, Max Schwarz, Sven Behnke
Object pose estimation is a crucial prerequisite for robots to perform autonomous manipulation in clutter. Real-world bin-picking settings such as warehouses present additional cha…
Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench
Max Schwarz, David Droeschel, Christian Lenz +7
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state-of-the-art in autonomous operation of ground-based an…
Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing
Max Schwarz, Christian Lenz, Germán Martín García +4
Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage…
RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
Max Schwarz, Anton Milan, Arul Selvam Periyasamy +1
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many…