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Wout Boerdijk

7 accepted papers

2026

Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM

Markus Rueggeberg, Maximilian Ulmer, Maximilian Durner, Wout Boerdijk, Marcus Gerhard Müller, Rudolph Triebel +1
ICRA 2026poster

The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of p…

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2025

Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation

Maximilian Ulmer, Wout Boerdijk, Rudolph Triebel, Maximilian Durner
ICCV 2025poster

This paper presents OC-DiT, a novel class of diffusion models designed for object-centric prediction, and applies it to zero-shot instance segmentation. We propose a conditional latent diffusion framework that generates instance masks by conditioning the generative process on object templates and im…

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2025

Towards Autonomous Data Annotation and System-Agnostic Robotic Grasping Benchmarking with 3D-Printed Fixtures

Wout Boerdijk, Maximilian Durner, Ryo Sakagami, Peter Lehner, Rudolph Triebel
ICRA 2025

The interaction of robots with their environment requires robust object-centric perception capabilities, typically achieved using learning-based methods trained on synthetic data. However, real-world deployment demands evaluating these capabilities in relevant environments, often involving extensive

Cited by 0Source ↗code ↗Scholar ↗
2024

Unknown Object Grasping for Assistive Robotics

Elle Miller, Maximilian Durner, Matthias Humt, Gabriel Quere, Wout Boerdijk, Ashok M. Sundaram +2
ICRA 2024poster

We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of…

Cited by 2Source ↗Scholar ↗
2021

Unknown Object Segmentation from Stereo Images

Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltán-Csaba Márton, Rudolph Triebel
IROS 2021poster

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and seve…

Cited by 40Source ↗code ↗Scholar ↗
2021

“What’s This?” - Learning to Segment Unknown Objects from Manipulation Sequences

Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel
ICRA 2021poster

We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed by a distinction between manipulator and object solely by observing the motion between consecutive RGB frames. In contr…

Cited by 7Source ↗code ↗Scholar ↗
2020

Self-Supervised Object-in-Gripper Segmentation from Robotic Motions

Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel
CoRL 2020

Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to seg

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