3 accepted papers
LiDAR Simultaneous Localization and Mapping (SLAM) has been pivotal in various domains, including digital twins, geographic surveying, and autonomous mobile robotics. However, achieving an optimal balance between high precision and computational efficiency remains a significant challenge. In this pa
Underwater monocular SLAM is a challenging problem with applications from autonomous underwater vehicles to marine archaeology. However, existing underwater SLAM methods struggle to produce maps with high-fidelity rendering. In this paper, we propose WaterSplat-SLAM, a novel monocular underwater SLA
We present MPDG-SLAM, a novel 3D Gaussian point cloud rendering SLAM method based on Motion Probability (MP) for dynamic interference handling. Current 3DGSSLAM approaches for dynamic environments often rely on optical flow estimation masks. However, these deep learning-based optical flow models are