23 citations · 52 across the 8 of their papers we have counts for
8 papers
Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion
Christopher L. Choi, Binbin Xu, Stefan Leutenegger
Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles, and augmented and virtual reality applications. In order to use them for any computer vision or state-e…
What to Learn: Features, Image Transformations, or Both?
Yuxuan Chen, Binbin Xu, Frederike Dümbgen +1
Long-term visual localization is an essential problem in robotics and computer vision, but remains challenging due to the environmental appearance changes caused by lighting and se…
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Xingxing Zuo, Nan Yang, Nathaniel Merrill +2
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently b…
Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV
Sotiris Papatheodorou, Nils Funk, Dimos Tzoumanikas +3
Exploration of unknown space with an autonomous mobile robot is a well-studied problem. In this work we broaden the scope of exploration, moving beyond the pure geometric goal of u…
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes
Binbin Xu, Andrew J. Davison, Stefan Leutenegger
In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete…
Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation
Yifei Ren, Binbin Xu, Christopher L. Choi +1
In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system. Even in extremely dynamic scenes, it can robustly optimise for the came…
Deep Probabilistic Feature-metric Tracking
Binbin Xu, Andrew J. Davison, Stefan Leutenegger
Dense image alignment from RGB-D images remains a critical issue for real-world applications, especially under challenging lighting conditions and in a wide baseline setting. In th…
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…