160 citations · 628 across the 19 of their papers we have counts for
9 papers · 1 filter
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
Binbin Xu, Wenbin Li, Dimos Tzoumanikas +3
We propose a new multi-instance dynamic RGB-D SLAM system using an object-level octree-based volumetric representation. It can provide robust camera tracking in dynamic environment…
Task-Embedded Control Networks for Few-Shot Imitation Learning
Stephen James, Michael Bloesch, Andrew J. Davison
Much like humans, robots should have the ability to leverage knowledge from previously learned tasks in order to learn new tasks quickly in new and unfamiliar environments. Despite…
LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo
Ronald Clark, Michael Bloesch, Jan Czarnowski +2
Sum-of-squares objective functions are very popular in computer vision algorithms. However, these objective functions are not always easy to optimize. The underlying assumptions ma…
Fusion++: Volumetric Object-Level SLAM
John McCormac, Ronald Clark, Michael Bloesch +2
We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor…
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bruno Bodin, Harry Wagstaff, Sajad Saeedi +9
SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify…
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
Jan Matas, Stephen James, Andrew J. Davison
We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformabl…