8 accepted papers
Visual-inertial odometry (VIO) has made significant progress in various applications. However, one of the key challenges in VIO is the efficient and robust fusion of visual and inertial measurements, particularly while mitigating the impact of sensor failures. To address this challenge, we propose a
Recently, camera-based solutions have been extensively explored for scene semantic completion (SSC). Despite their success in visible areas, existing methods struggle to capture complete scene semantics due to frequent visual occlusions. To address this limitation, this paper presents the first sate…
One of the key challenges of visual Simultaneous Localization and Mapping (SLAM) in large-scale environments is how to effectively use global localization to correct the cumulative errors from long-term tracking. This challenge presents itself in two main aspects: first, the difficulty for robots in…
Performing accurate localization while maintaining the low-level communication bandwidth is an essential challenge of multi-robot simultaneous localization and mapping (MR-SLAM). In this paper, we tackle this problem by generating a compact yet discriminative feature descriptor with minimum inferenc…
Depth completion plays a crucial role in enabling real-world applications such as obstacle avoidance and SLAM for robot navigation. This letter focuses on addressing the depth completion challenge for single-line LiDAR, commonly used in conjunction with visual cameras. The sparsity of valid depth po
As vision based perception methods are usually built on the normal light assumption, there will be a serious safety issue when deploying them into low light environments. Recently, deep learning based methods have been proposed to enhance low light images by penalizing the pixel-wise loss of low lig
The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spati…
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appeara