Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM

2026-06-08

AI Summary

This work addresses the challenge of achieving high-precision, full six-degree-of-freedom (6-DoF) egomotion estimation from the asynchronous event streams produced by event cameras—a task at which conventional frame-based motion estimation algorithms struggle. The paper presents the first algebraic minimal five-point solver tailored for asynchronous event data. By modeling asynchronous optical flow, the method decouples the differential epipolar constraint into angular and linear velocity components and formulates a system of algebraic equations based on a first-order rotational dynamics approximation. Leveraging polynomial solving techniques combined with acceleration strategies, the solver achieves significantly improved computational efficiency. Experiments demonstrate that the proposed approach outperforms existing synchronous methods on both synthetic and real-world datasets, delivering superior accuracy and robustness under spatiotemporal noise, making it well-suited for real-time motion estimation in high-speed robotic applications.

Abstract

As a bio-inspired intelligent sensor, event cameras have introduced a new paradigm in the intelligent perception of spatiotemporal information and visual motion estimation, characterized by their high temporal resolution, low latency, and minimal power consumption. However, their asynchronous data streams present significant challenges to traditional synchronous, frame-based algorithms. To address these challenges, this paper presents a novel framework for full degree of freedom (DoF) egomotion estimation directly from asynchronous optical flow, specifically targeting the joint recovery of angular and linear velocities. We decouple the differential epipolar constraint into distinct angular and linear velocity components, and derive its formulation for asynchronous data. Based on this formulation, an optimization algorithm is developed that enables full-DoF egomotion estimation leveraging at least five points. Furthermore, by applying a first-order approximation to rotational dynamics, we transform the constraint equations into a polynomial form, resulting in the first algebraic minimal 5-point solver for this formulation. To ensure real-time performance in high-speed scenarios, we additionally propose an accelerated solver achieved by truncating high-order angular velocity terms. Extensive evaluations on both synthetic and real-world datasets demonstrate that the asynchronous approach outperforms traditional synchronous methods, particularly in its accuracy and robustness to spatiotemporal noise. We believe that this work establishes a critical foundation for efficient and accurate continuous-time motion estimation in high-speed robotics applications.

Problem

• event cameras
• asynchronous data
• full-DoF motion estimation
• egomotion
• optical flow

Innovation

• event cameras
• asynchronous optical flow
• full-DoF motion estimation
• minimal solver
• differential epipolar constraint