PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments (2411.15800v1)

Abstract: Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other works express dynamic objects by point clouds, sparse joints, or coarse meshes, which fails to provide a photo-realistic representation. To overcome the above limitations, we propose a photo-realistic and geometry-aware RGB-D SLAM method by extending Gaussian splatting. Our method is composed of three main modules to 1) map the dynamic foreground including non-rigid humans and rigid items, 2) reconstruct the static background, and 3) localize the camera. To map the foreground, we focus on modeling the deformations and/or motions. We consider the shape priors of humans and exploit geometric and appearance constraints of humans and items. For background mapping, we design an optimization strategy between neighboring local maps by integrating appearance constraint into geometric alignment. As to camera localization, we leverage both static background and dynamic foreground to increase the observations for noise compensation. We explore the geometric and appearance constraints by associating 3D Gaussians with 2D optical flows and pixel patches. Experiments on various real-world datasets demonstrate that our method outperforms state-of-the-art approaches in terms of camera localization and scene representation. Source codes will be publicly available upon paper acceptance.

• The paper introduces PG-SLAM, a novel RGB-D SLAM system utilizing Gaussian splatting to accurately map and localize in dynamic environments containing moving people and objects.
• PG-SLAM employs distinct modules for modeling dynamic foreground elements using shape priors and static background with appearance constraints, enhancing overall scene understanding.
• Numerical results demonstrate PG-SLAM's superior performance over existing methods in dynamic settings, achieving lower camera localization errors and accurately representing complex moving structures.

An Overview of PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments

Simultaneous localization and mapping (SLAM) is a fundamental task in robotics and autonomous systems, enabling autonomous navigation in unknown environments. While SLAM has succeeded in static settings, dynamic environments pose significant challenges due to moving entities that disrupt traditional geometric constraints. The paper "PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments" addresses these challenges by introducing a novel approach that incorporates both photo-realistic scene representation and enhanced localization accuracy through the use of Gaussian splatting.

Core Contributions

The paper's primary contribution is the development of a photo-realistic and geometry-aware SLAM system, PG-SLAM, that effectively handles dynamic scenes, including non-rigid humans and rigid objects. This is achieved through three key modules:

1. Dynamic Foreground Mapping:
   • The system leverages Gaussian splatting with shape priors and geometric constraints to model dynamic entities comprehensively. Non-rigid human modeling harnesses the Skinned Multi-Person Linear (SMPL) model, allowing for nuanced deformation capture, while rigid items are tracked using optical flows and aligned dynamically.
2. Static Background Mapping:
   • A local map structure optimizes static background modeling through appearance constraints, ensuring efficient handling of sequences with consistent observations. The optimization strategy between neighboring local maps integrates both geometric and appearance constraints, allowing for effective error reduction and alignment.
3. Camera Localization:
   • The approach innovatively leverages data from both static and dynamic components to improve camera localization. By associating 3D Gaussians with 2D optical flows, the system offsets noise and improves accuracy. The integration of a two-stage localization strategy allows for effective initial pose estimation and subsequent refinement using dynamic foreground data.

Numerical Results and Performance

The paper validates PG-SLAM’s performance using several real-world datasets, demonstrating superior results to state-of-the-art methods such as E-SLAM and MonoGS. Key improvements are seen in environments with significant dynamic interference, where PG-SLAM accurately models both static and dynamic elements without simplification or omission, achieving lower absolute trajectory errors in camera localization.

Methodological Insights

PG-SLAM's reliance on Gaussian splatting is particularly noteworthy for enhancing rendering performance and geometric explainability over neural radiance fields (NeRF). By rendering Gaussians as RGB and depth images, the photometric and depth errors are minimized, allowing for robust SLAM operations even when faced with dynamic visual input.

Furthermore, the integration of pose variation recognition in human dynamics through neural networks adapts to real-time changes, extending the system's applicability in dynamic, real-world scenes. This capacity to incorporate both articulated constraints and geometric adjustments for non-rigid entities sets a significant precedent in differentiable rendering applications in SLAM.

Implications and Future Directions

The implications of PG-SLAM are broad, with potential applications spanning robotics, augmented reality, and autonomous navigation across varied environments. By overcoming the limitations of prior SLAM systems in dynamic settings, PG-SLAM opens new pathways for more sophisticated autonomous systems that can function seamlessly in real-life scenarios involving complex and variable moving structures.

Future developments may explore further integration of deep learning models for enhanced feature recognition and environmental adaptability. Additionally, extending this framework to handle multiple interacting dynamic entities simultaneously could provide even greater utility and accuracy, particularly in crowded and unpredictable settings.

In conclusion, PG-SLAM marks a significant advance in the field of SLAM by incorporating comprehensive dynamic scene understanding through innovative use of Gaussian splatting. Its capacity to maintain accuracy and detail in dynamic environments positions it as a crucial development in autonomous system technologies.