2 citations · 2 across the 1 of their papers we have counts for
7 papers
On the Potential of Smarter Multi-layer Maps
Francesco Verdoja, Ville Kyrki
The most common way for robots to handle environmental information is by using maps. At present, each kind of data is hosted on a separate map, which complicates planning because a…
Beyond Top-Grasps Through Scene Completion
Jens Lundell, Francesco Verdoja, Ville Kyrki
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the wor…
Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration
Tobias Zaenker, Francesco Verdoja, Ville Kyrki
Modern intelligent and autonomous robotic applications often require robots to have more information about their environment than that provided by traditional occupancy grid maps.…
Robust Grasp Planning Over Uncertain Shape Completions
Jens Lundell, Francesco Verdoja, Ville Kyrki
We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object a…
Deep Network Uncertainty Maps for Indoor Navigation
Francesco Verdoja, Jens Lundell, Ville Kyrki
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full…
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
Jens Lundell, Francesco Verdoja, Ville Kyrki
Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass p…