In the context of automated driving, navigating through challenging urban environments with
dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving …

Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in
settings with variable elements, which demand the integration of multiple sensors to ensure …

Video semantic segmentation has achieved great success, which is significant for road
scene understanding. However, semantic segmentation remains challenging in poor …

Memory-based networks have achieved tremendous success in video object segmentation.
However, these methods still suffer from unfaithful segmentation and inferior efficiency under …

Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of
autonomous driving and robotics. One crucial component of visual SLAM is the Field-of …

The autonomous navigation of robots depends on the capabilities in map creation, precise
positioning, path planning, and obstacle avoidance. However, the complex environment of …

Abstract Monocular Simultaneous Landing and Mapping (SLAM), Visual Odometry (VO),
and Structure from Motion (SFM) are techniques that have emerged recently to address the …

Existing methods for video object segmentation (VOS) have achieved significant success by
performing semantic guidance, spatial constraint, or temporal consistency. However, VOS …

Indoor localization has long been a challenging task due to the complexity and dynamism of
indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and …

We introduce a new algorithm that utilizes semantic information to enhance feature matching
in visual SLAM pipelines. The proposed method constructs a high-dimensional semantic …