arXiv subjects

Yannick Burkhardt

Publications and source records attributed to Yannick Burkhardt.

4 recordsLinked to original sources

arXiv2026

AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM

The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments. Although their high temporal resolution does not require synchronous processing, most event-based odometry methods still run at fixed rates, which simplifies system design but restricts latency and throughput. In this work, we present AERO-VIS, a stereo event-inertial SLAM system with an integrated, data-driven, robust, and performance-optimized keypoint detector. By processing the event stream asynchronously, the system dynamically adapts to downstream runtime demands, ensuring low-latency and real-time performance. When deploying AERO-VIS on a UAV, we achieve unprecedented accuracy in onboard event-based SLAM. These unique characteristics enable us to present the first purely event-based inertial SLAM system that demonstrates closed-loop UAV control and large-scale state estimation while relying solely on onboard compute. A video of the experiments and the source code are available at ethz-mrl.github.io/AERO-VIS.

cs.RO↗

arXiv2025

Actron3D: Learning Actionable Neural Functions from Videos for Transferable Robotic Manipulation

We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from just a few monocular, uncalibrated, RGB-only human videos. At its core lies the Neural Affordance Function, a compact object-centric representation that distills actionable cues from diverse uncalibrated videos-geometry, visual appearance, and affordance-into a lightweight neural network, forming a memory bank of manipulation skills. During deployment, we adopt a pipeline that retrieves relevant affordance functions and transfers precise 6-DoF manipulation policies via coarse-to-fine optimization, enabled by continuous queries to the multimodal features encoded in the neural functions. Experiments in both simulation and the real world demonstrate that Actron3D significantly outperforms prior methods, achieving a 14.9 percentage point improvement in average success rate across 13 tasks while requiring only 2-3 demonstration videos per task.

cs.RO↗

arXiv2025

SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM

Event-based keypoint detection and matching holds significant potential, enabling the integration of event sensors into highly optimized Visual SLAM systems developed for frame cameras over decades of research. Unfortunately, existing approaches struggle with the motion-dependent appearance of keypoints and the complex noise prevalent in event streams, resulting in severely limited feature matching capabilities and poor performance on downstream tasks. To mitigate this problem, we propose SuperEvent, a data-driven approach to predict stable keypoints with expressive descriptors. Due to the absence of event datasets with ground truth keypoint labels, we leverage existing frame-based keypoint detectors on readily available event-aligned and synchronized gray-scale frames for self-supervision: we generate temporally sparse keypoint pseudo-labels considering that events are a product of both scene appearance and camera motion. Combined with our novel, information-rich event representation, we enable SuperEvent to effectively learn robust keypoint detection and description in event streams. Finally, we demonstrate the usefulness of SuperEvent by its integration into a modern sparse keypoint and descriptor-based SLAM framework originally developed for traditional cameras, surpassing the state-of-the-art in event-based SLAM by a wide margin. Source code is available at https://ethz-mrl.github.io/SuperEvent/.

cs.CV↗

arXiv2023

Multi-fingered Dynamic Grasping for Unknown Objects

Dexterous grasping of unseen objects in dynamic environments is an essential prerequisite for the advanced manipulation of autonomous robots. Prior advances rely on several assumptions that simplify the setup, including environment stationarity, pre-defined objects, and low-dimensional end-effectors. Though easing the problem and enabling progress, it undermined the complexity of the real world. Aiming to relax these assumptions, we present a dynamic grasping framework for unknown objects in this work, which uses a five-fingered hand with visual servo control and can compensate for external disturbances. To establish such a system on real hardware, we leverage the recent advances in real-time dexterous generative grasp synthesis and introduce several techniques to secure the robustness and performance of the overall system. Our experiments on real hardware verify the ability of the proposed system to reliably grasp unknown dynamic objects in two realistic scenarios: objects on a conveyor belt and human-robot handover. Note that there has been no prior work that can achieve dynamic multi-fingered grasping for unknown objects like ours up to the time of writing this paper. We hope our pioneering work in this direction can provide inspiration to the community and pave the way for further algorithmic and engineering advances on this challenging task. A video of the experiments is available at https://youtu.be/b87zGNoKELg.

cs.RO↗