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Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees

Authors: Alberto Mazzoni, Calogero M. Oddo, Giacomo Valle, Domenico Camboni, Ivo Strauss, Massimo Barbaro, Gianluca Barabino, Roberto Puddu, Caterina Carboni, Lorenzo Bisoni, Jacopo Carpaneto, Fabrizio Vecchio, Francesco M. Petrini, Simone Romeni, Tamas Czimmermann, Luca Massari, Riccardo di Iorio, Francesca Miraglia, Giuseppe Granata, Danilo Pani, Thomas Stieglitz, Luigi Raffo, Paolo M. Rossini & Silvestro Micera

 

Abstract

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

Reference

  • Published in:Scientific Reports (Volume 10, Article number: 527, 2020)
  • DOI: 10.1038/s41598-020-57454-4 yy
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  • Date: 2020
Posted on: January 21, 2020

On the Redundancy Detection in Keyframe-based SLAM

Authors: Schmuck, Patrik; Chli, Margarita

 

Abstract

Egomotion and scene estimation is a key component in automating robot navigation, as well as in virtual reality applications for mobile phones or head-mounted displays. It is well known, however, that with long exploratory trajectories and multi-session mapping for long-term autonomy or collaborative applications, the maintenance of the ever-increasing size of these maps quickly becomes a bottleneck. With the explosion of data resulting in increasing runtime of the optimization algorithms ensuring the accuracy of the Simultaneous Localization And Mapping (SLAM) estimates, the large quantity of collected experiences is imposing hard limits on the scalability of such techniques. Considering the keyframe-based paradigm of SLAM techniques, this paper investigates the redundancy inherent in SLAM maps, by quantifying the information of different experiences of the scene as encoded in keyframes. Here we propose and evaluate different information-theoretic and heuristic metrics to remove dispensable scene measurements with minimal impact on the accuracy of the SLAM estimates. Evaluating the proposed metrics in two state-of-the-art centralized collaborative SLAM systems, we provide our key insights into how to identify redundancy in keyframe-based SLAM.

Reference

  • Published in: 2019 International Conference on 3D Vision (3DV)
  • DOI: 10.1109/3DV.2019.00071
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  • Date: 2019
Posted on: January 16, 2020

Events-to-Video: Bringing Modern Computer Vision to Event Cameras

  • Authors: Rebecq, Henri; Ranftl, René; Koltun, Vladlen; Scaramuzza, Davide

Event cameras are novel sensors that report brightness changes in the form of asynchronous “events” instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and no motion blur. Since the output of event cameras is fundamentally different from conventional cam-eras, it is commonly accepted that they require …

Posted on: November 12, 2019