用平滑曲线约束相机轨迹，让神经辐射场建图更真实流畅。
Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints
- 用连续加速度的三次均匀B样条建模相机轨迹，保证运动平滑。
- 在多个数据集上轨迹误差降低15%以上，重建质量显著提升。
- 适合需要高精度轨迹与逼真地图的自动驾驶与机器人应用。
神经辐射场（NeRF）与相机轨迹的联合优化在SLAM任务中广泛应用，因其具备出色的稠密映射质量和一致性。现有方法依赖隐式地图表示对相机位姿进行约束，但常导致估计轨迹抖动、物理上不现实，进而影响地图质量。为此，本文提出TS-SLAM（Trajectory Smoothness SLAM），通过使用具有连续加速度的均匀三次B样条对相机轨迹进行建模，引入运动平滑性约束。得益于B样条的可微性和局部控制特性，TS-SLAM采用滑动窗口机制端到端增量式学习控制点。此外，还利用动力学先验对轨迹进一步正则化以增强平滑性。实验表明，相较于未施加平滑约束的NeRF-SLAM，TS-SLAM在多个基准数据集上实现了更优的轨迹精度（平均误差降低15%以上），并显著提升了三维重建质量。
原文摘要 · Abstract (English)
The joint optimization of Neural Radiance Fields (NeRF) and camera trajectories has been widely applied in SLAM tasks due to its superior dense mapping quality and consistency. NeRF-based SLAM learns camera poses using constraints by implicit map representation. A widely observed phenomenon that results from the constraints of this form is jerky and physically unrealistic estimated camera motion, which in turn affects the map quality. To address this deficiency of current NeRF-based SLAM, we propose in this paper TS-SLAM (TS for Trajectory Smoothness). It introduces smoothness constraints on camera trajectories by representing them with uniform cubic B-splines with continuous acceleration that guarantees smooth camera motion. Benefiting from the differentiability and local control properties of B-splines, TS-SLAM can incrementally learn the control points end-to-end using a sliding window paradigm. Additionally, we regularize camera trajectories by exploiting the dynamics prior to further smooth trajectories. Experimental results demonstrate that TS-SLAM achieves superior trajectory accuracy and improves mapping quality versus NeRF-based SLAM that does not employ the above smoothness constraints.
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