1 code implementation • 27 Feb 2018 • Francesco Verdoja, Marco Grangetto
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation.
1 code implementation • 17 Dec 2020 • Jens Lundell, Enric Corona, Tran Nguyen Le, Francesco Verdoja, Philippe Weinzaepfel, Gregory Rogez, Francesc Moreno-Noguer, Ville Kyrki
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
2 code implementations • 2 Mar 2019 • Jens Lundell, Francesco Verdoja, Ville Kyrki
We present a method for planning robust grasps over uncertain shape completed objects.
Robotics
1 code implementation • 31 May 2018 • Jens Lundell, Francesco Verdoja, Ville Kyrki
However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring.
1 code implementation • 13 Sep 2018 • Francesco Verdoja, Jens Lundell, Ville Kyrki
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation.
1 code implementation • 6 Aug 2020 • Francesco Verdoja, Ville Kyrki
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness.
1 code implementation • 23 Aug 2022 • Francesco Verdoja, Tomasz Piotr Kucner, Ville Kyrki
Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in.
no code implementations • 15 Sep 2019 • 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 workspace to top-grasps.
no code implementations • 16 Oct 2020 • Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka, Ville Kyrki
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition.
no code implementations • 8 Mar 2021 • Jens Lundell, Francesco Verdoja, Ville Kyrki
Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps.