Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers
This addresses a critical limitation in event-based vision for robotics and autonomous systems by enabling full motion estimation without additional hardware, though it is incremental as it builds on existing sparse geometric solvers.
The paper tackles the problem of estimating full six-degree-of-freedom egomotion for event cameras without relying on known rotations or extra sensors, achieving this by proposing solvers based on event manifolds from line segments and demonstrating effectiveness on synthetic and real-world data.
For event cameras, current sparse geometric solvers for egomotion estimation assume that the rotational displacements are known, such as those provided by an IMU. Thus, they can only recover the translational motion parameters. Recovering full-DoF motion parameters using a sparse geometric solver is a more challenging task, and has not yet been investigated. In this paper, we propose several solvers to estimate both rotational and translational velocities within a unified framework. Our method leverages event manifolds induced by line segments. The problem formulations are based on either an incidence relation for lines or a novel coplanarity relation for normal vectors. We demonstrate the possibility of recovering full-DoF egomotion parameters for both angular and linear velocities without requiring extra sensor measurements or motion priors. To achieve efficient optimization, we exploit the Adam framework with a first-order approximation of rotations for quick initialization. Experiments on both synthetic and real-world data demonstrate the effectiveness of our method. The code is available at https://github.com/jizhaox/relpose-event.