DROID-ANCHOR: Odometry-Anchored Recurrent Metric Depth Estimation

• URL: http://arxiv.org/abs/2607.17058v1
• Date: Sun, 19 Jul 2026 04:03:34 GMT
• Title: DROID-ANCHOR: Odometry-Anchored Recurrent Metric Depth Estimation
• Abstract summary: Metric-DROID is an end-to-end recurrent architecture that anchors visual SLAM to physical reality by integrating proprioceptive odometry.<n>Our system intelligently balances visual re-projection and metric translation residuals, effectively mitigating the impact of wheel-slip and sensor noise.
• Score: 12.05347499107643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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