Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

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.

• The paper introduces TS-SLAM, which integrates cubic B-splines to model continuous camera trajectories, mitigating jerky motion in NeRF-based SLAM.
• It employs an end-to-end sliding window approach to optimize control points, leading to refined localization and improved mapping quality.
• Experimental results on datasets like ScanNet and TUM RGB-D show notable reductions in trajectory error metrics and enhanced reconstruction performance.

Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

The paper "Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints" presents an innovative approach designed to address the challenges associated with jerky and physically unrealistic camera trajectories often observed in Neural Radiance Fields-based Simultaneous Localization and Mapping (NeRF-SLAM) systems. The authors introduce a novel system, TS-SLAM (Trajectory Smoothness SLAM), which leverages smoothness constraints to enhance both trajectory accuracy and mapping quality.

Core Contributions

The primary contribution of this paper is the integration of B-splines to model camera trajectories in SLAM systems, providing a smooth representation that is differentiable and controlled locally. This methodology mitigates the prevalent issue of discontinuous camera motion in existing NeRF-SLAM paradigms, which can significantly degrade the quality of maps and impede precise localization.

1. B-splines Representation: By representing camera trajectories with uniform cubic B-splines, TS-SLAM indirectly imposes smoothness constraints. B-splines offer $C^2$ continuity, ensuring that trajectories maintain a natural flow and remain physically realistic. This stands in contrast to the jerky and unrealistic paths produced by constraints in traditional methods.
2. End-to-End Learning: The system learns control points end-to-end using a sliding window paradigm, capitalizing on the local control properties of B-splines. This approach enables incremental optimization, crucial for maintaining performance in dynamic SLAM environments.
3. Dynamics Regularization: By introducing a dynamics regularization that accounts for object acceleration, the paper emphasizes enforcing physically plausible motion constraints. This not only stabilizes trajectory estimation but also enhances overall system robustness by incorporating physical priors.
4. Local Bundle Adjustment: To refine pose estimates further, a local bundle adjustment is utilized. This allows for a hierarchical optimization of the map and control points, incrementally improving trajectory smoothness and subsequently mapping accuracy.

Experimental Results

Experimental evaluations on datasets such as ScanNet, TUM RGB-D, and NeuralRGBD demonstrate the efficacy of TS-SLAM compared to existing SLAM systems like iMAP, NICE-SLAM, and Co-SLAM. TS-SLAM outperforms these baselines in terms of trajectory accuracy, with notable improvements in both Absolute Trajectory Error (ATE) and Relative Pose Error (RPE).

1. Tracking Accuracy: TS-SLAM shows reductions in ATE and RPE, highlighting its ability to produce smoother trajectory estimations. This is a significant advancement over the baseline systems that suffer from drift and local trajectory inaccuracies.
2. Reconstruction Quality: The use of smooth trajectories leads to better map reconstruction, evidenced by improved Depth L1 metrics and greater completion ratios in visual reconstructions.

Implications and Future Work

The implications of this work are manifold for both practical and theoretical aspects of SLAM systems:

• Practical Applications: Improved trajectory estimation directly impacts the effectiveness of robotic navigation and scene understanding, critical components for autonomous systems in dynamic environments.
• Theoretical Advancements: By incorporating B-splines and physics-aware constraints, this paper paves the way for more integrated and reliable approaches that could benefit from temporally coherent data assimilation across various sensory modalities.

The authors also acknowledge potential limitations due to the fixed time intervals and spline order of the employed B-splines. Future research could explore adaptive methods for dynamically adjusting these parameters to further optimize performance under varying conditions.

In summary, this paper advances the field of SLAM by presenting TS-SLAM, which effectively integrates trajectory smoothness constraints into NeRF-based SLAM systems, enhancing the accuracy of both localization and mapping tasks.