7 accepted papers
Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence a
Visual-inertial systems have been widely studied and applied in the last two decades (from the early 2000 s to the present), mainly due to their low cost and power consumption, small footprint, and high availability. Such a trend simultaneously leads to a large amount of visual-inertial calibration
Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability. However, there is a lack of research in fusing these learning-based methods with multi-sensor information, which could b
The inertial measurement unit (IMU), as an interoceptive sensor typically providing high-frequency angular velocity and specific force measurements, has been widely exploited for accurate motion estimation in modern robotic applications, such as autonomous navigation and exploration. Recently, there
Continuous and reliable ego-velocity information is significant for high-performance motion control and planning in a variety of robotic tasks, such as autonomous navigation and exploration. While linear velocities as first-order kinematics can be simultaneously estimated with other states or explic
Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenari
High-definition (HD) map is crucial for intelligent vehicles to perform high-level localization and navigation. To improve the availability and usability of HD map, it is meaningful to investigate crowd-sourced mapping solutions and low-cost map-aided localization schemes which don't rely on high-en