On the Benefits of Visual Stabilization for Frame- and Event-based Perception
This addresses performance issues in robot perception systems where mechanical stabilization is not feasible, offering a software-based solution with measurable gains.
The paper tackles the problem of vision-based perception performance degradation under large camera orientation changes by proposing a processing-based stabilization approach for both frames and events, showing improvements of 27.37% in feature tracking accuracy, 34.82% in ego-motion estimation accuracy, and at least 25% reduction in processing time.
Vision-based perception systems are typically exposed to large orientation changes in different robot applications. In such conditions, their performance might be compromised due to the inherent complexity of processing data captured under challenging motion. Integration of mechanical stabilizers to compensate for the camera rotation is not always possible due to the robot payload constraints. This paper presents a processing-based stabilization approach to compensate the camera's rotational motion both on events and on frames (i.e., images). Assuming that the camera's attitude is available, we evaluate the benefits of stabilization in two perception applications: feature tracking and estimating the translation component of the camera's ego-motion. The validation is performed using synthetic data and sequences from well-known event-based vision datasets. The experiments unveil that stabilization can improve feature tracking and camera ego-motion estimation accuracy in 27.37% and 34.82%, respectively. Concurrently, stabilization can reduce the processing time of computing the camera's linear velocity by at least 25%. Code is available at https://github.com/tub-rip/visual_stabilization