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.

翻译：神经辐射场（NeRF）与相机轨迹的联合优化因其卓越的稠密建图质量与一致性，已在SLAM任务中得到广泛应用。基于NeRF的SLAM通过隐式地图表征的约束来学习相机位姿。由此类约束形式导致的一个普遍现象是：估计的相机运动存在抖动且不符合物理真实性，进而影响地图质量。为应对当前基于NeRF的SLAM的这一不足，本文提出TS-SLAM（TS指轨迹平滑性）。该方法通过采用具有连续加速度的均匀三次B样条表示相机轨迹，引入轨迹平滑性约束，从而保证相机运动的平滑性。得益于B样条的可微性与局部控制特性，TS-SLAM能够基于滑动窗口范式端到端地增量学习控制点。此外，我们通过利用动力学先验对相机轨迹进行正则化，以进一步平滑轨迹。实验结果表明，相较于未采用上述平滑性约束的基于NeRF的SLAM方法，TS-SLAM实现了更优的轨迹精度并提升了建图质量。