5 accepted papers
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle with tracking and reconstruction in dynamic environments, such as real-world scenes with moving elements. Existing NeRF-b
In dynamic scenes, achieving accurate camera localization and reconstructing a long-term consistent map containing only the static background are two major challenges faced by Visual Simultaneous Localization and Mapping (VSLAM). In current traditional dynamic VSLAM systems, the methods used to hand…
The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynam…
Semantic simultaneous localization and mapping (SLAM) is a popular technology enabling indoor mobile robots to sufficiently perceive and interact with the environment. In this paper, we propose an object-aware semantic SLAM system, which consists of a quadric initialization method, an object-level d
Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map matching-base