# Differential 6-DOF Pose Estimation with Provable First-Order Immunity to Camera Calibration Errors

arXiv: https://arxiv.org/abs/2608.04673 · HTML: https://mlsanity.com/paper/2608.04673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [An Efficient Algebraic Solution to the Perspective-Three-Point Problem](https://mlsanity.com/paper/1701.08237/) (2017)
- [HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction](https://mlsanity.com/paper/2411.17982/) (2024)
- [CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Camera](https://mlsanity.com/paper/2409.10441/) (2024)
- [DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding](https://mlsanity.com/paper/2403.11776/) (2024)
- [POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method](https://mlsanity.com/paper/1904.04858/) (2019)
- [From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation](https://mlsanity.com/paper/2312.05995/) (2023)
- [CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination](https://mlsanity.com/paper/2209.05824/) (2022)
- [Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem](https://mlsanity.com/paper/2408.01945/) (2024)
- [Optimal DLT-based Solutions for the Perspective-n-Point](https://mlsanity.com/paper/2410.14164/) (2024)
- [Bias-Eliminated PnP for Stereo Visual Odometry: Provably Consistent and Large-Scale Localization](https://mlsanity.com/paper/2504.17410/) (2025)

## Built on by (most cited first, 2 of 2)

- [Beyond Control Points: Arcsecond Relative-Motion Estimation of Vision Measurement Platforms With Incomplete or Absent Control Fields](https://mlsanity.com/paper/2608.13918/) (2026)
- [Spaceborne differential photogrammetry for control-free measurement of large-gradient deformation with structural immunity and a predictable accuracy envelope](https://mlsanity.com/paper/2609.29550/) (2026)

## Abstract (arXiv)

Accurate six-degree-of-freedom (6-DOF) motion estimation is essential for robotic manipulation, autonomous systems, and structural displacement monitoring. Conventional 3D-2D methods estimate absolute camera poses independently at each time and recover platform motion through camera-to-platform extrinsics, making them sensitive to extrinsic calibration errors, especially for micromotion. We present a differential pose estimation method that directly recovers platform motion from inter-frame image displacements and known 3D control points. By differencing perspective projection equations, using a depth-invariance approximation, and modeling motion on SE(3), the method avoids independent absolute-pose estimation and supports both monocular and multi-camera systems. We prove that translational extrinsic errors cancel exactly, while rotational errors induce a bounded perturbation determined by calibration error, motion magnitude, and observation geometry. We also derive generic observability conditions, a Cramer-Rao lower bound, and a bias-eliminated consistent estimator, and characterize the validity limits of the approximations. Extensive synthetic and real-world experiments establish a new state of the art for 6-DOF platform micromotion estimation, outperforming representative PnP and generalized-PnP methods in accuracy, calibration robustness, and computational efficiency. With five control points and 0.5-pixel image noise, the monocular solver obtains a combined pitch-yaw rotation RMSE of 10.09 arcsec, a translation RMSE of 3.70 mm, and a runtime of 0.34 ms. The binocular solver achieves a rotation RMSE of 10.58 arcsec, a translation RMSE of 3.91 mm, and a runtime of 0.27 ms. Code will be released upon publication at https://github.com/zyoungszu/pami2026.