3 accepted papers
Autonomous navigation social robots need to track pedestrian movements in real-time with high precision to optimize path planning and avoid collisions. However, the main challenge of pedestrian tracking lies in the significant variations in human posture, which differ from rigid-body structures like
High-fidelity reconstruction is crucial for dense SLAM. Recent popular methods utilize 3D Gaussian splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods ignore issues of detail and consistency in different parts of the scene. To address this, we p
Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-w