44 citations · 96 across the 16 of their papers we have counts for
8 papers · 1 filter
IN-Sight: Interactive Navigation through Sight
Philipp Schoch, Fan Yang, Yuntao Ma +3
Current visual navigation systems often treat the environment as static, lacking the ability to adaptively interact with obstacles. This limitation leads to navigation failure when…
Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping
Simon Boche, Sebastián Barbas Laina, Stefan Leutenegger
Autonomous navigation is one of the key requirements for every potential application of mobile robots in the real-world. Besides high-accuracy state estimation, a suitable and glob…
FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object
Hanzhi Chen, Binbin Xu, Stefan Leutenegger
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categor…
Anthropomorphic Grasping with Neural Object Shape Completion
Diego Hidalgo-Carvajal, Hanzhi Chen, Gemma C. Bettelani +6
The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is wel…
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…
Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion
Simon Boche, Xingxing Zuo, Simon Schaefer +1
Robotic applications are continuously striving towards higher levels of autonomy. To achieve that goal, a highly robust and accurate state estimation is indispensable. Combining vi…