# Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems

arXiv: https://arxiv.org/abs/2606.09477 · HTML: https://mlsanity.com/paper/2606.09477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 8 Jun 2026.

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

- [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)
- [Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera](https://mlsanity.com/paper/2408.03225/) (2024)
- [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)
- [Minimal Solutions for Relative Pose with a Single Affine Correspondence](https://mlsanity.com/paper/1912.10776/) (2019)
- [RePoseD: Efficient Relative Pose Estimation With Known Depth Information](https://mlsanity.com/paper/2501.07742/) (2025)
- [Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement](https://mlsanity.com/paper/2506.00578/) (2025)
- [Relative Pose from Deep Learned Depth and a Single Affine Correspondence](https://mlsanity.com/paper/2007.10082/) (2020)
- [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)
- [Efficient Relative Pose Estimation for Cameras and Generalized Cameras in Case of Known Relative Rotation Angle](https://mlsanity.com/paper/1901.11357/) (2019)
- [Trifocal Tensor and Relative Pose Estimation with Known Vertical Direction](https://mlsanity.com/paper/2512.19110/) (2025)

## Abstract (arXiv)

Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mobile devices, and unmanned aerial vehicles (UAVs). However, existing solutions often suffer from high computational complexity or rely on an excessive number of point correspondences, limiting their real-world applicability. To address these limitations, we propose two efficient minimal solvers for estimating the relative poses of multi-camera systems using a novel parameterization. The first solver leverages the vertical direction prior provided by Inertial Measurement Units (IMUs), while the second utilizes the rotation axis direction prior from IMUs. Our methods require only four point correspondences and reduce the problem of multi-camera relative pose estimation to solving a univariate 6th-degree polynomial, a significant improvement over existing approaches, which typically involve 8th-degree polynomials. This reduction in computational complexity and correspondence requirements makes our solvers particularly effective when integrated into RANSAC frameworks, demonstrating strong potential for visual odometry applications. Through rigorous evaluations on synthetic data and the KITTI benchmark, our methods achieved superior computational efficiency and competitive accuracy compared to state-of-the-art algorithms.