197 citations · 292 across the 54 of their papers we have counts for
5 papers · 1 filter
Flexible Disaster Response of Tomorrow -- Final Presentation and Evaluation of the CENTAURO System
Tobias Klamt, Diego Rodriguez, Lorenzo Baccelliere +29
Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promisin…
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Xi Chen, Ali Ghadirzadeh, Mårten Björkman +1
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs. However, it's applica…
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci +2
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitab…
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction
Jiexiong Tang, John Folkesson, Patric Jensfelt
In this paper, we proposed a new deep learning based dense monocular SLAM method. Compared to existing methods, the proposed framework constructs a dense 3D model via a sparse to d…
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
Jiexiong Tang, Ludvig Ericson, John Folkesson +1
In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D pr…