DROID-ANCHOR: Odometry-Anchored Recurrent Metric Depth Estimation

2026-07-19 · 04:03 UTC ·Yuxuan Chen, Brook Du ·1 min read

Brief

Metric-DROID tackles monocular scale ambiguity and drift by anchoring recurrent flow-based SLAM to physical odometry. It introduces an LSTM Update Operator that embeds high-frequency odometry into spatial features and an uncertainty-aware backend (BAodom) that learns heteroscedastic Σo to weight visual versus odometry residuals. The approach claims improved metric alignment and robustness to wheel-slip without retraining.

Why it matters

Metric-DROID is an end-to-end recurrent depth/SLAM architecture that anchors monocular vision to metric scale by integrating proprioceptive odometry; it uses an LSTM Update Operator to encode high-frequency odometry sequences into spatial feature maps, providing a persistent metric bias for iterative depth refinement.

Key details

The system includes an Uncertainty-Aware Metric Backend (BA_odom) that regresses a time-varying heteroscedastic covariance Σ_o to balance visual reprojection and metric translation residuals, mitigating wheel-slip and sensor noise; a selective residual fine-tuning strategy preserves pretrained geometric priors while enabling zero-shot metric alignment.

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.