A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras

2026-06-08

AI Summary

This work addresses the challenge of jointly recovering six-degree-of-freedom (6-DoF) absolute pose and velocity with high accuracy from event cameras, a task where existing geometric methods—primarily focused on velocity estimation—fall short. We propose a novel geometric framework leveraging 3D lines and event constraints, exploiting the orthogonality between event plane normals and 3D lines as well as the collinearity of events and their 2D projections. This formulation enables, for the first time, the independent solution of either 6-DoF absolute pose or velocity using only three event–line correspondences. To realize this, we develop an efficient linear solver, a polynomial solver guaranteeing globally optimal rotation, and optimization strategies to enhance velocity estimation accuracy. Experiments demonstrate that our method significantly outperforms state-of-the-art approaches on both synthetic and real-world datasets, achieving leading performance in both precision and computational efficiency.

Abstract

Despite the rapid advancements in event-based motion estimation, current geometric methods primarily focus on velocity estimation. However, absolute pose estimation, which is equally crucial for key applications such as robotic navigation and augmented reality, remains relatively underexplored. Consequently, the simultaneous recovery of absolute pose and velocity from event streams remains an open and challenging problem. To address this gap, we propose a geometric framework for absolute pose and velocity estimation by leveraging 3D lines in the scene and the events they trigger. At the core of the framework lie two key geometric constraints: the orthogonality between a 3D line and the normal vector of its corresponding event plane, and the collinearity of an event with the 2D projection of its associated line. Based on these constraints, we present both linear and polynomial solvers for absolute pose estimation. The former enables efficient computation, while the latter provides a globally optimal solution for rotation. For velocity estimation, we develop an efficient linear solver and a more accurate optimization-based solver to recover both angular and linear velocities. Notably, our methods require a minimum of three event-line correspondences to determine the 6-DoF absolute pose or velocities independently. Extensive experiments in simulation and on real-world datasets demonstrate that our methods achieve state-of-the-art performance, with significant improvements in accuracy and computational efficiency compared to existing methods. The demo code is publicly available at https://github.com/Zibin6/EventPoseVelocity.

Problem

• absolute pose estimation
• velocity estimation
• event cameras
• geometric constraints
• 6-DoF motion

Innovation

• event cameras
• absolute pose estimation
• velocity estimation
• geometric constraints
• 3D line correspondences