When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency

Technical Summary: When Do Learned Priors Help Visual–Inertial Estimation?

Problem Statement
The integration of learned components (e.g., pose, depth, bias, uncertainty) into geometric visual–inertial estimators (VIO) is increasingly common. However, a central ambiguity exists in evaluating these systems: when a fused system outperforms a standalone learned network or a direct IMU integration, it is unclear whether the improvement stems from the genuine utility of the learned prior or from changes in the backend, calibration strategy, initialization, temporal association, or evaluation metrics. Existing comparisons often conflate the value of the complete visual–inertial system with the incremental contribution of a specific learned measurement. Furthermore, learned predictions are not automatically compatible with metric backends; mismatches in scale, reference frame, temporal intervals, or uncertainty can lead to ignored residuals or forced disagreements in trajectory, velocity, and bias states.

Methodology
The authors propose a controlled evaluation framework designed to isolate the incremental value of a learned prior from the benefits of the underlying geometric backend. The core principle is to hold all variables constant—sensor streams, timestamps, initialization windows, frontend, backend, calibration conventions, and output gauges—while varying only the presence and weighting of the learned factor.

• System Instantiation: The framework is instantiated using a representative learned monocular motion prior (derived from a MonoViT-based pose predictor) integrated into an otherwise unchanged VINS-Fusion backend. The learned relative-motion output is incorporated as a local relative-motion factor.
• Evidence Layers: The evaluation distinguishes four distinct evidence layers to avoid conflating different types of performance:
  1. Local Motion Consistency: Measured via Relative Pose Error (RPE).
  2. Global Trajectory Accuracy: Measured via Absolute Pose Error (APE) under declared SE(3) or Sim(3) alignment.
  3. Physical-State Correctness: Evaluation of extrinsics, scale, gravity, bias, and synchronization against physical references.
  4. Numerical/Implementation Consistency: Backend cost, convergence, repeatability, and loop-closure response.
• Experimental Variables: The study compares "Original VINS" against "VINS+Pose" (with the learned prior) under matched conditions. It further investigates:
  • Fixed accurate camera–IMU extrinsics vs. online updating (self-calibration).
  • Initialization sensitivity and perturbation.
  • Cross-sequence behavior (KITTI and EuRoC).
  • The impact of loop closure on global correction.

Key Results
The controlled experiments yield several counter-intuitive findings regarding the utility of learned priors:

1. Negligible Incremental Value: Under fixed accurate extrinsics, adding the learned pose prior to the VINS backend does not provide a consistent trajectory improvement. On the representative KITTI sequence, the translation APE RMSE changed from 31.4 m (Original VINS) to 31.8 m (VINS+Pose). Across a four-recording stress panel, the mean APE change was only −0.2%. Increasing the weight of the prior by five times actually degraded performance by 8.2%.
2. Local vs. Global Discrepancy: The learned prior preserves the local motion consistency (RPE) of the original system but fails to improve global accuracy (APE). Conversely, releasing accurate extrinsics for online updating drastically increased global APE (by ~45–52%) while leaving local RPE changes minimal (<2%). This indicates that added calibration freedom primarily affects accumulated global states rather than immediate frame-to-frame tracking.
3. Initialization and Calibration Dependence: The backend can refine a reasonable initial solution but cannot reliably recover a physically plausible calibration after entering a poor coupled state. Large initial extrinsic perturbations often lead to convergence near the noisy initialization rather than the ground truth.
4. Decoupled Metrics: Low RPE does not imply low APE. Similarly, a low backend optimization cost in matched runs does not guarantee physically correct calibration or scale. Repeatability does not imply absolute accuracy.
5. Loop Closure Sensitivity: The effect of loop closure is highly dependent on the consistency of the pre-loop trajectory and calibration. Enabling loop closure improved APE when extrinsics were accurate but degraded it when the underlying state contained calibration bias.

Key Contributions

1. Controlled Same-Backend Framework: The paper formulates a framework that explicitly separates system-level fusion benefits from the incremental value of learned information by holding sensor inputs, initialization, and backend configurations fixed.
2. Unified Evidence Framework: It establishes a multi-layered evaluation protocol distinguishing local consistency, global accuracy, physical correctness, and numerical behavior, arguing that agreement at one layer does not imply agreement at others.
3. Systematic Characterization of Interactions: The study characterizes the interaction between learned motion measurements, fixed vs. online extrinsics, initialization, and sequence conditions, identifying when online calibration acts as a local refinement versus when it introduces initialization-dependent drift.
4. Cross-Experiment Analysis: It provides evidence that local accuracy, numerical convergence, and repeatability must be reported as distinct properties rather than inferred from a single trajectory or objective value.

Significance and Claims
The paper claims that the usefulness of learned priors cannot be inferred from fusion performance alone. A better final trajectory does not necessarily identify whether the gain originates from the learned prior, the native geometric backend, or a changed evaluation protocol.

The authors assert that reliable evaluation requires same-backend controls and a joint consideration of prior compatibility, calibration, initialization, global drift, physical state error, and backend consistency. The results suggest that simply injecting a learned pose prior into a geometric backend without explicit scale alignment, uncertainty weighting, or confidence-aware selection may result in a redundant or inconsistent constraint that offers no measurable improvement over the original system. The study serves as a cautionary note against attributing system-level gains solely to learned components without rigorous isolation of variables.