activity
20182020
most citedVolumetric Instance-Aware Semantic Mapping and 3D Object Discovery

232 citations · 301 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2020★ 13 cited

LCD -- Line Clustering and Description for Place Recognition

Felix Taubner, Florian Tschopp, Tonci Novkovic +2

Current research on visual place recognition mostly focuses on aggregating local visual features of an image into a single vector representation. Therefore, high-level information…

cs.RO2020★ 19 cited

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

Julien Kindle, Fadri Furrer, Tonci Novkovic +3

Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a re…

cs.RO2019★ 30 cited

VersaVIS: An Open Versatile Multi-Camera Visual-Inertial Sensor Suite

Florian Tschopp, Michael Riner, Marius Fehr +7

Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as…

cs.RO2019★ 6 cited

Object Finding in Cluttered Scenes Using Interactive Perception

Tonci Novkovic, Remi Pautrat, Fadri Furrer +3

Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algori…

cs.RO2019★ 232 cited

Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

Margarita Grinvald, Fadri Furrer, Tonci Novkovic +4

To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides…

cs.RO2018

Incremental Object Database: Building 3D Models from Multiple Partial Observations

Fadri Furrer, Tonci Novkovic, Marius Fehr +5

Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surf…

cs.RO2018★ 1 cited

Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning

Michel Breyer, Fadri Furrer, Tonci Novkovic +2

Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects' variab…