6 accepted papers
Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research que…
Relative camera pose estimation between two images is a fundamental task in 3D computer vision. Recently, many relative pose estimation networks have been explored for learning a mapping from two input images to their corresponding relative pose, however, the estimated relative poses by these method…
The applicability of SLAM algorithms is largely limited to the assumption of scene rigidity. Moreover, dynamic object perception is an essential technique for many robotic applications such as autonomous driving and multi-robot collaboration. In this letter, we present PointSLOT, an online, real-tim
Point cloud registration is an essential technique for many tasks including autonomous navigation, augmented/virtual reality and scene reconstruction. In man-made environments, abundant plane features with structural regularities are commonly seen, which is actually beneficial for point cloud regist
In order to improve the accuracy of simultaneous localization and mapping problem, plane motion assumption is often used for advanced ground vehicle SLAM system. However, such an assumption is not always suitable to complex and changeable road scenes. In this letter, we propose a stereo-vision based
Simultaneous localization and object tracking (SLOT) is essentially important for autonomous systems. Tightly-coupled and loosely-coupled methods are two commonly used back-end frameworks for the state-of-the-art solutions of SLOT problem. However, some inherent limitations exist in these two framew