Semi-supervised video object segmentation is the task of segmenting the target in
sequential frames given the ground truth mask in the first frame. The modern approaches …

The key to autonomous navigation in unmanned systems is the ability to recognize static
and moving objects in the environment and to support the task of predicting the future state …

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and
Ranging) is an expanding field of research with numerous applications in the areas of …

Semantic segmentation of outdoor point clouds is an important task in the field of computer
vision, aiming to classify outdoor point cloud data into different semantic categories. The …

Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities
for mobile systems to achieve full autonomy. Several approaches have attempted to achieve …

We propose MoBox, a low-cost solution for semi-supervised video object segmentation that
requires only bounding boxes as manual annotations for training. Built upon a mature semi …

Point cloud-based place recognition can be used for global localization in large-scale
scenes and loop-closure detection in simultaneous localization and mapping (SLAM) …

Autonomous driving emphasizes precise multi-sensor fusion positioning on limit resource
embedded systems. LiDAR-centered sensor fusion system serves as a mainstream …

Dynamic objects diversify the distribution of point cloud in the map, degrading the
performance of the robotic downstream tasks. To address this problem, we present a novel …

Moving objects in the environment have a higher priority and more challenges in growing
domains like unmanned vehicles and intelligent robotics. Estimating the motion state of …