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.
新兴的 3D 场景表示方法，如神经辐射场（Neural Radiance Fields, NeRF）和 3D Gaussian Splatting (3DGS)，在基于高质量视频序列的同时定位与建图（Simultaneous Localization and Mapping, SLAM）任务中表现出了高效的真实感渲染能力。然而，现有方法在处理运动模糊帧时表现欠佳，这在现实场景中（如低光照或长曝光条件下）十分常见，往往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，我们提出了一种处理严重运动模糊输入的密集视觉 SLAM 管道——MBA-SLAM。该方法结合了一种高效的运动模糊感知追踪器（motion blur-aware tracker）和基于神经辐射场或高斯分布的建图器（mapper）。通过准确建模运动模糊图像的物理生成过程，该方法能够同时学习 3D 场景表示并估计曝光时间内相机的局部轨迹，从而主动补偿因相机运动导致的运动模糊。 在实验中，我们验证了 MBA-SLAM 在相机定位和地图重建方面优于当前最先进的方法。无论是在包含清晰图像的数据集还是存在运动模糊的数据集中（包括合成和真实数据集），我们的方法均表现出卓越性能，展示了其卓越的通用性和鲁棒性。