4 accepted papers
Visual inertial odometry (VIO) is a popular solution for localization in the GPS-denied environment. However, current VIO algorithms' performance degrades in the visual degraded environment due to limited visual features. This paper presents a robust VIO algorithm with learned velocity constraints.
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
Visual Inertial Odometry (VIO) is a widely studied localization technique in robotics. State-of-the-art VIO algorithms are composed of two parts: a frontend which performs visual perception and inertial measurement pre-processing, and a backend which fuses vision and inertial measurements to estimat…
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