NSL-SLAM: Integrating Monocular Depth Priors Cuts Depth RMSE by 35%
zhenjun_zhao · x · 2026-07-30
NSL-SLAM introduces a practical SLAM system tailored for high-fidelity structured-light depth sensing.
- Enhanced Depth Sensing: By incorporating strong monocular depth priors into structured-light stereo decoding, it reduces depth RMSE by 35% on the Replica-SL dataset compared to the previous NSL method.
- Depth-Centric Pipeline: Because structured-light geometry is dense and metrically accurate, the system uses it as the primary tracking signal. It only adds sparse visual correspondences for geometrically degenerate cases and lightweight bundle adjustment for long-range drift.
Experiments show that under shared-depth conditions, this system achieves the best tracking accuracy and improves reconstruction F-score by 1.6 points over the SOTA baseline.
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