Point cloud based retrieval for place recognition is still a challenging problem since the
drastic appearance changes of scenes due to seasonal or artificial changes in the …

3D visual recognition is a prerequisite for most autonomous robotic systems operating in the
real world. It empowers robots to perform a variety of tasks, such as tracking, understanding …

Visual localization plays an important role for intelligent robots and autonomous driving,
especially when the accuracy of GNSS is unreliable. Recently, camera localization in LiDAR …

Accurate determination of a robot's location, which is referred to as place recognition, is
essential for achieving autonomous navigation. However, complex real-world environments …

Since the early 1950s, the development of human settlements and over-exploitation of
agriculture in the China side of the Amur River Basin (CARB) have had a major impact on …

Simultaneous localization and mapping (SLAM) is used in solving the problems of
localization, navigation, and map construction for autonomous vehicles moving in unknown …

Due to the huge volume of point cloud data, storing or transmitting it is currently difficult and
expensive in autonomous driving. Learning from the high efficiency video coding (HEVC) …

The application of deep learning is blooming in the field of visual place recognition, which
plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

Light Detection and Ranging (LiDAR), an active remote sensing technology, is becoming an
essential tool for geoinformation extraction and urban planning. Airborne Laser Scanning …