Learned priors show limited benefit for visual inertial estimation accuracy
When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency
RoboticsComputer Vision and Pattern Recognition
Summary
Estimating motion and position using camera and motion sensors can be improved by combining learned guesses with traditional methods. This paper carefully tests when these learned guesses actually help versus when they don’t by keeping everything else the same in the system. The authors found that adding learned priors did not significantly improve accuracy and sometimes made results worse if weighted too heavily. They argue that to fairly judge learned priors, you must control other factors like sensor calibration and initialization together.
What this means in practice
- •For robotics engineers: Evaluate whether integrating learned motion priors improves robot localization accuracy without changing existing backend systems.
- •For augmented reality developers: Assess the impact of learned priors on camera-IMU pose estimation stability to improve AR device tracking.
Authors
Jinchang Zhang, Guoyu Lu
Abstract
Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence cues. Yet it remains unclear whether gains arise from useful learned priors or from changes in the backend, calibration, initialization, temporal association, or evaluation gauge. We present a controlled framework for learning-augmented visual--inertial estimation that separates fusion gain from the incremental value of a learned prior and evaluates four evidence layers: local motion consistency, global trajectory accuracy, physical-state correctness, and numerical consistency. We instantiate the framework with a MonoViT-based monocular motion prior added as a local relative-motion factor to an unchanged VINS backend. We compare Original VINS and learned-prior VINS under matched sensor streams, timestamps, initialization, frontend/backend settings, and camera--IMU extrinsics, while probing calibration, initialization, state coupling, scale, bundle adjustment, and loop closure. On KITTI, with fixed reference extrinsics, translation APE RMSE is 31.4 m for Original VINS and 31.8 m with the learned prior. Across four recordings, the prior changes mean APE by only -0.2%, while a five-times-higher weight worsens it by 8.2%. Online extrinsic updates increase mean APE by 45.7% and 52.1%, respectively, while mean RPE changes by less than 2%. These results show that fusion performance alone cannot establish the value of learned priors. Reliable evaluation requires same-backend controls and joint analysis of prior compatibility, calibration, initialization, global drift, physical-state error, and backend consistency.