Reliable multi-agent trajectory prediction is crucial for the safe planning and control of
autonomous systems. Compared with single-agent cases, the major challenge in …

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor
data. The respectively used principle of measuring distances provides advantages and …

Place recognition technology can eliminate cumulative errors and thus plays a vital role in
autonomous driving. In this paper, the composite feature of point cloud and image data is …

Localization on 3D data is a challenging task for unmanned vehicles, especially in long-term
dynamic urban scenarios. Due to the generality and long-term stability, the pole-like objects …

Simultaneous Localization and Mapping (SLAM) is a critical foundation to a wide variety of
robotic applications. Visual SLAM systems rely on Visual Place Recognition (VPR) for map …

The development of fully autonomous driving vehicles has become a key focus for both
industry and academia over the past decade, fostering significant progress in situational …

The present prevailing loop closure detection algorithm is mainly applicable for
simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon …