# Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications

arXiv: https://arxiv.org/abs/2606.09569 · HTML: https://mlsanity.com/paper/2606.09569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [MAGSAC++, a fast, reliable and accurate robust estimator](https://mlsanity.com/paper/1912.05909/) (2019)
- [Progressive NAPSAC: sampling from gradually growing neighborhoods](https://mlsanity.com/paper/1906.02295/) (2019)
- [Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation](https://mlsanity.com/paper/2306.12996/) (2023)
- [Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement](https://mlsanity.com/paper/2506.00578/) (2025)
- [Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences](https://mlsanity.com/paper/2007.10700/) (2020)
- [Six-Point Method for Multi-Camera Systems with Reduced Solution Space](https://mlsanity.com/paper/2402.18066/) (2024)
- [Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurement](https://mlsanity.com/paper/2511.18317/) (2025)
- [Ridge Estimation-Based Vision and Laser Ranging Fusion Localization Method for UAVs](https://mlsanity.com/paper/2512.16314/) (2025)
- [Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurement](https://mlsanity.com/paper/2601.12682/) (2026)
- [A DMD-Based Adaptive Modulation Method for High Dynamic Range Imaging in High-Glare Environments](https://mlsanity.com/paper/2602.12044/) (2026)
- [Trifocal Tensor and Relative Pose Estimation with Known Vertical Direction](https://mlsanity.com/paper/2512.19110/) (2025)

## Abstract (arXiv)

With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.