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.

翻译：新兴的三维场景表示方法，如神经辐射场（NeRF）和三维高斯泼溅（3DGS），已在同时定位与建图（SLAM）中展现出实现照片级真实感渲染的有效性，尤其是在使用高质量视频序列作为输入时。然而，现有方法难以处理运动模糊帧，这类帧在现实场景（如低光照或长曝光条件下）中十分常见。这通常会导致相机定位精度和地图重建质量显著下降。为应对这一挑战，我们提出了一种稠密视觉SLAM流程（即MBA-SLAM）来处理严重运动模糊的输入。我们的方法将高效的运动模糊感知跟踪器与基于神经辐射场或高斯泼溅的建图器相结合。通过精确建模运动模糊图像的物理成像过程，我们的方法能够同时学习三维场景表示并估计相机在曝光期间内的局部轨迹，从而主动补偿由相机运动引起的运动模糊。在实验中，我们证明MBA-SLAM在相机定位和地图重建方面均优于以往最先进的方法，在一系列数据集（包括包含清晰图像以及受运动模糊影响的合成与真实数据集）上均表现出优越性能，凸显了我们方法的通用性与鲁棒性。代码发布于 https://github.com/WU-CVGL/MBA-SLAM。