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

2026-06-08

AI Summary

This work addresses the high computational complexity and substantial point correspondence requirements that often hinder practical deployment of relative pose estimation in multi-camera systems. The authors propose two efficient minimal solvers that, for the first time, incorporate IMU-derived priors—either the vertical direction or a known rotation axis—into a minimal solver framework. Requiring only four point correspondences, the approach leverages a novel parametrization and algebraic geometry techniques to reduce the problem to solving a univariate sextic polynomial, a significant simplification over existing octic formulations. Integrated within a RANSAC-based robust estimation pipeline, extensive experiments on synthetic data and the KITTI benchmark demonstrate that the proposed method achieves comparable accuracy while substantially lowering computational cost and data requirements.

Abstract

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.

Problem

• visual-inertial
• relative pose estimation
• multi-camera systems
• minimal solvers
• computational complexity

Innovation

• minimal solver
• visual-inertial odometry
• multi-camera system
• IMU prior
• polynomial reduction