Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

TL;DR

This paper introduces TS-SLAM, a method that enforces smooth camera trajectories using B-splines and dynamics priors, significantly enhancing the accuracy and quality of NeRF-based SLAM systems.

Contribution

The paper proposes a novel smoothness constraint for NeRF-based SLAM using B-splines and dynamics priors, improving trajectory and map quality.

Findings

TS-SLAM achieves higher trajectory accuracy.

Mapping quality is improved with smooth trajectories.

The method effectively enforces physically realistic camera motions.

Abstract

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.