6 accepted papers
The 3D perception of satellites, including both their shape and pose, is a key foundation for robotic on-orbit servicing. However, the demanding space environment-such as intense and dim illumination-presents significant challenges. Previous non-cooperative methods focus on specific geometric featur
Multivariate time series (MTS) forecasting endeavors to anticipate the forthcoming sequence of interdependent variables through the utilization of past observations. The prevailing methodologies, relying on deep neural networks, Transformer, or information bottleneck frameworks, persist in confronti
Dense depth estimation is an important task for applications, such as object detection, 3-D reconstruction, etc. Stereo matching, as a popular method for dense depth estimation, has been faced with challenges when low textures, occlusions or domain gaps exist. Stereo-LiDAR fusion has recently become
Hand-eye calibration estimates the pose of a camera relative to a robot, which is a fundamental problem for visually guided robots, especially for dynamic object grasping. Most methods use 2D fiducial markers with distinctive visual features and require pre-calibration for accurate calibration, whic…
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this letter
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this letter, we propose a hierarchical driving model with explicit models of