Search Results for author:
Found 8 papers, 7 papers with code
AirIMU: Learning Uncertainty Propagation for Inertial Odometry
Accurate uncertainty estimation for inertial odometry is the foundation to achieve optimal fusion in multi-sensor systems, such as visual or LiDAR inertial odometry.
AirObject: A Temporally Evolving Graph Embedding for Object Identification
In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects.
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly.
AirDOS: Dynamic SLAM benefits from Articulated Objects
To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects.
Bridging Graph Network to Lifelong Learning with Feature Interaction
In this paper, we aim to bridge GNN to lifelong learning, which is to overcome the effect of ``catastrophic forgetting" for continuously learning a sequence of graph-structured tasks.
Lifelong Graph Learning
In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNN).
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
In this paper, we explore the problem of interesting scene prediction for mobile robots.
TartanAir: A Dataset to Push the Limits of Visual SLAM
We present a challenging dataset, the TartanAir, for robot navigation task and more.
Robotics
