🤖 AI Summary
This work addresses the scale ambiguity and drift inherent in monocular depth estimation, which hinder its applicability to robot navigation requiring metric-scale accuracy. The authors propose Metric-DROID, an end-to-end recurrent visual SLAM architecture that anchors depth estimates to physical scale by fusing egomotion from proprioceptive odometry. Key innovations include LSTM-based modeling of high-frequency odometry sequences, an uncertainty-aware bundle adjustment backend (BA_odom) that integrates odometry measurements into geometric constraints via covariance-weighted anchoring, and a selective residual fine-tuning strategy enabling zero-shot metric alignment. This approach effectively mitigates wheel slippage and sensor noise while preserving visual geometric consistency, thereby significantly improving both accuracy and robustness in metric depth estimation.
📝 Abstract
Precise metric depth estimation is fundamental for autonomous robot navigation, yet monocular systems inherently suffer from scale ambiguity and scale drift. While recent recurrent flow-based SLAM systems have demonstrated state-of-the-art robustness, they remain scale-ambiguous. In this paper, we propose Metric-DROID, an end-to-end recurrent architecture that anchors visual SLAM to physical reality by integrating proprioceptive odometry. Our framework introduces the following innovations: (1) A LSTM Update Operator that encodes high-frequency odometry sequences into spatial feature maps, providing a persistent metric bias for iterative refinement. (2) An Uncertainty-Aware Metric Backend ($BA_{odom}$) that treats odometry as a geometric anchor with learned heteroscedastic covariance. By regressing a time-varying metric uncertainty $Σ_{o}$, our system intelligently balances visual re-projection and metric translation residuals, effectively mitigating the impact of wheel-slip and sensor noise. (3) We further propose a selective residual fine-tuning strategy to preserve pre-trained geometric priors while enabling zero-shot metric alignment.