MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation (2411.08279v1)

Abstract: Emerging 3D scene representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated their effectiveness in Simultaneous Localization and Mapping (SLAM) for photo-realistic rendering, particularly when using high-quality video sequences as input. However, existing methods struggle with motion-blurred frames, which are common in real-world scenarios like low-light or long-exposure conditions. This often results in a significant reduction in both camera localization accuracy and map reconstruction quality. To address this challenge, we propose a dense visual SLAM pipeline (i.e. MBA-SLAM) to handle severe motion-blurred inputs. Our approach integrates an efficient motion blur-aware tracker with either neural radiance fields or Gaussian Splatting based mapper. By accurately modeling the physical image formation process of motion-blurred images, our method simultaneously learns 3D scene representation and estimates the cameras' local trajectory during exposure time, enabling proactive compensation for motion blur caused by camera movement. In our experiments, we demonstrate that MBA-SLAM surpasses previous state-of-the-art methods in both camera localization and map reconstruction, showcasing superior performance across a range of datasets, including synthetic and real datasets featuring sharp images as well as those affected by motion blur, highlighting the versatility and robustness of our approach. Code is available at https://github.com/WU-CVGL/MBA-SLAM.

• The paper introduces a novel blur-aware SLAM pipeline that models motion-blurred images and estimates camera trajectories accurately.
• It integrates advanced neural radiance fields and 3D Gaussian splatting to boost rendering quality and mapping efficiency.
• Experiments on synthetic and real datasets show improved accuracy in camera localization and mapping compared to state-of-the-art methods.

An Analysis of MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation

The paper, "MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation", presents a compelling approach to addressing the challenges posed by motion blur in Simultaneous Localization and Mapping (SLAM) systems. By integrating Radiance Fields and 3D Gaussian Splatting into a specialized SLAM framework, the authors propose MBA-SLAM, a novel pipeline designed to maintain accuracy in localization and mapping even with motion-blurred inputs.

Technical Contributions

The principal contribution of the paper is the development of a robust SLAM methodology that efficiently handles motion blur by incorporating a motion blur-aware tracker and a deblurring mapping process. The authors accurately model the physical formation of motion-blurred images and utilize this understanding to predict the motion trajectory of cameras within the exposure time.

1. Radiance Fields and Gaussian Splatting Integration: The paper bridges the gap between emerging 3D scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), leveraging their strengths within dense SLAM systems. This integration improves representation accuracy and efficiency, facilitating high-fidelity photo-realistic rendering.
2. Motion Blur-Aware Tracker: The tracker estimates the camera's local motion trajectory during the exposure period, leveraging a non-linear model that improves robustness against significant camera motion changes.
3. Enhanced SLAM Pipeline Performance: By combining the motion blur-aware tracker with a novel bundle adjustment approach, the proposed system demonstrates enhanced performance in both camera localization and map reconstruction across both synthetic and real-world datasets.

Experimental Evaluation

The authors present a thorough evaluation of MBA-SLAM, showcasing its superior performance over existing state-of-the-art SLAM methods. The experiments, conducted on a variety of datasets including both motion-blurred and sharp scenarios, reinforce the proposed system's robustness and versatility. For instance, in synthetic datasets like ArchViz, MBA-SLAM achieves top accuracy in tracking and mapping, despite challenging motion patterns and blurred inputs.

Quantitatively, MBA-SLAM's robust handling of motion blur results in significant improvements in Absolute Trajectory Error (ATE) and rendering quality metrics such as PSNR and SSIM when compared with competitive methods. This highlights the effectiveness of its innovative blur-aware mechanisms.

Implications and Future Directions

The implications of this work are significant for applications in fields where SLAM operates under suboptimal lighting or high-speed movements, such as autonomous driving, robotics, and augmented reality. The integration of motion blur awareness into SLAM offers a pathway toward more reliable and practical SLAM solutions in dynamic, real-world environments.

The paper points towards potential future developments such as the refinement of trajectory models using higher-order splines for even more precise motion capture. Further exploration into optimization of the computational resources, especially concerning the number of virtual frames, could enhance system performance, offering real-time capabilities even with more extensive scene complexities.

Overall, this work represents a notable contribution to the area of dense visual SLAM by effectively tackling the pervasive issue of motion blur, paving the way for more resilient and adaptive SLAM systems.