UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments

Abstract: Recent 3D Gaussian Splatting (3DGS) techniques for Visual Simultaneous Localization and Mapping (SLAM) have significantly progressed in tracking and high-fidelity mapping. However, their sequential optimization framework and sensitivity to dynamic objects limit real-time performance and robustness in real-world scenarios. We present UP-SLAM, a real-time RGB-D SLAM system for dynamic environments that decouples tracking and mapping through a parallelized framework. A probabilistic octree is employed to manage Gaussian primitives adaptively, enabling efficient initialization and pruning without hand-crafted thresholds. To robustly filter dynamic regions during tracking, we propose a training-free uncertainty estimator that fuses multi-modal residuals to estimate per-pixel motion uncertainty, achieving open-set dynamic object handling without reliance on semantic labels. Furthermore, a temporal encoder is designed to enhance rendering quality. Concurrently, low-dimensional features are efficiently transformed via a shallow multilayer perceptron to construct DINO features, which are then employed to enrich the Gaussian field and improve the robustness of uncertainty prediction. Extensive experiments on multiple challenging datasets suggest that UP-SLAM outperforms state-of-the-art methods in both localization accuracy (by 59.8%) and rendering quality (by 4.57 dB PSNR), while maintaining real-time performance and producing reusable, artifact-free static maps in dynamic environments.The project: https://aczheng-cai.github.io/up_slam.github.io/

Overview of UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments

The paper introduces UP-SLAM, a sophisticated RGB-D SLAM system tailored for dynamic environments. Building upon recent advancements in 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF), UP-SLAM addresses the limitations of previous SLAM systems that often assume static environments, a premise that hampers performance in real-world scenarios characterized by dynamic elements. UP-SLAM distinguishes itself through a parallelized framework that decouples tracking and mapping to maintain real-time performance, an essential feature for the deployment in robotics.

Core Contributions

The primary contributions of this paper revolve around the development of an adaptively structured SLAM system capable of robust performance in dynamic conditions. The system employs an innovative probabilistic octree to manage Gaussian primitives without traditional hand-crafted thresholds, thereby enhancing computational efficiency and map fidelity. It introduces a training-free uncertainty estimator that leverages multi-modal residuals to estimate motion uncertainty at a per-pixel level, enabling the effective handling of open-set dynamic objects.

The system's architecture includes a temporal encoder designed to augment rendering quality, and the integration of DINO visual features through a shallow multilayer perceptron (MLP) to strengthen the Gaussian field representation. As a result, UP-SLAM yields substantial improvements in localization accuracy, boasting a 59.8% enhancement compared to state-of-the-art methods, and improves rendering quality by 4.57 dB in PSNR.

Implications and Future Prospects

From a practical standpoint, UP-SLAM represents a significant advancement in the SLAM field, especially for applications in robotics and autonomous systems navigating dynamic environments. The adaptive Gaussian representation coupled with uncertainty prediction enables the construction of high-quality, reusable static maps devoid of artifacts induced by transient objects. This capability is particularly beneficial for tasks involving scene understanding and navigation where dynamic interactions are prevalent.

The theoretical implications of this work include the exploration of probabilistic models in SLAM for dynamic object detection and scene reconstruction. The approach of decoupling tracking from mapping while utilizing real-time uncertainty estimation opens avenues for designing more efficient and scalable SLAM systems. Furthermore, the integration of robust feature extraction mechanisms, like DINO, paves the way for enhancing semantic richness in map representations.

Future developments may look towards optimizing the computational demands of uncertainty estimation further and exploring more sophisticated probabilistic models for dynamic object filtering. Additionally, extending this work to integrate with emerging neural SLAM methods could offer insights into achieving more comprehensive scene understanding across both dynamic and static contexts.

In summary, UP-SLAM’s architecture and methodologies contribute significantly to overcoming the inherent challenges in dynamic SLAM, offering both practical applications and theoretical advancements for future research endeavors in AI and robotics.