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Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
(2022)2022 26th International Conference on Pattern Recognition (ICPR)Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set, for tasks such as object tracking, pose estimation, grasping and manipulation, as there are datasets and pre-learned methods available for these objects. This limits the impact of our research since learning-based computer vision methods can only be used in ...Conference Paper -
Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields
(2022)This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning. We show that by adding the capacity to infer occupancy in a radius to a pre trained NeRF we are effectively learning an approximation to a Euclidean Signed Distance Field (ESDF). Even more, using backward differentiation of the network, we readily obtain the obstacle gradients that are ...Conference Paper -
Closed-Loop Next-Best-View Planning for Target-Driven Grasping
(2022)IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)Picking a specific object from clutter is an essential component of many manipulation tasks. Partial observations often require the robot to collect additional views of the scene before attempting a grasp. This paper proposes a closed-loop next-best-view planner that drives exploration based on occluded object parts. By continuously predicting grasps from an up-to-date scene reconstruction, our policy can decide online to finalize a grasp ...Conference Paper -
Collaborative Robot Mapping using Spectral Graph Analysis
(2022)2022 International Conference on Robotics and Automation (ICRA)In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots ...Conference Paper