The recent developments in the field of Simultaneous Localization and Mapping (SLAM) and related technologies indicate a significant shift towards integrating advanced computational techniques with traditional sensor data to enhance both the accuracy and efficiency of mapping and localization tasks. A notable trend is the adoption of neural fields and 3D Gaussian Splatting (3DGS) techniques, which are being increasingly utilized to improve the quality of dense mapping and photorealistic rendering in real-time applications. These methods are proving to be particularly effective in overcoming the limitations of traditional approaches, such as the inability to capture detailed scene geometry and the high computational costs associated with real-time processing.

Another key development is the integration of multiple sensor modalities, including LiDAR, inertial, and visual data, to achieve more robust and accurate state estimation. This multi-sensor fusion approach leverages the complementary strengths of each sensor type, enabling systems to perform well in a variety of challenging environments. Additionally, there is a growing emphasis on the development of datasets and frameworks that support fine-grained 3D applications, facilitating advancements in areas such as architectural reconstruction and virtual tourism.

Innovative solutions are also being proposed to address specific challenges in the field, such as the need for efficient exploration and mapping in complex environments, the handling of dynamic objects in real-world scenes, and the optimization of computational resources for deployment on embedded systems. These advancements are not only improving the performance of SLAM systems but are also expanding their applicability to a wider range of real-world scenarios.

Noteworthy Papers

• KN-LIO: Introduces a novel approach that combines geometric kinematics with neural fields to enhance simultaneous state estimation and dense mapping capabilities, demonstrating superior performance in high-dynamic environments.
• Scaffold-SLAM: Delivers high-quality photorealistic mapping across different camera types by introducing innovative techniques for modeling image appearance variations and guiding the distribution of Gaussians.
• SP-SLAM: Achieves real-time dense SLAM with improved reconstruction quality through the use of scene priors and an effective optimization strategy for mapping.
• ActiveGAMER: Leverages the efficient rendering capabilities of 3DGS for active mapping, significantly surpassing existing approaches in geometric and photometric accuracy.
• CULTURE3D: Presents a large-scale, fine-grained dataset that supports a wide range of 3D applications, promoting innovation in 3D modeling and analysis.
• SplatMAP: Integrates dense SLAM with 3DGS for real-time, high-fidelity dense reconstruction, achieving state-of-the-art results among monocular systems.
• PO-GVINS: Proposes a tightly coupled GNSS-visual-inertial positioning framework that achieves accurate, drift-free state estimation in challenging environments.
• VINGS-Mono: A monocular Gaussian Splatting SLAM framework designed for large scenes, demonstrating superior mapping and rendering quality in both indoor and outdoor environments.
• GS-LIVO: Introduces a real-time Gaussian-based SLAM system that leverages multi-sensor fusion for robust localization and dense mapping, deployable on resource-constrained embedded systems.