Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM
2026-06-08 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method to estimate movement using data from event cameras, which capture changes in a scene very quickly and asynchronously. They separate the camera's rotation and movement to better understand how it is moving in 3D space. Their approach uses at least five points and includes a special solver that works fast enough for real-time applications. Tests show their method is more accurate and reliable than older frame-based methods, especially in fast-moving situations.
event camerasegomotion estimationoptical flowdifferential epipolar constraintangular velocitylinear velocityasynchronous datareal-time optimizationminimal solverspatiotemporal noise
Authors
Shuo Pan, Banglei Guan, Bin Li, Zhenbao Yu, Zibin Liu, Zi Wang, Yang Shang, Qifeng Yu
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.