Event-based Motion & Appearance Fusion for 6D Object Pose Tracking
This work addresses the need for robust 6D pose tracking in highly dynamic environments, where traditional RGB-D cameras suffer from motion blur and low frame rates.
The authors propose a learning-free 6D object pose tracking method that fuses event-based motion (via optical flow) with appearance-based template correction. It achieves comparable or better performance than state-of-the-art algorithms for fast-moving objects, demonstrating the potential of event cameras in highly dynamic scenarios.
Object pose tracking is a fundamental and essential task for robotics to perform tasks in the home and industrial settings. The most commonly used sensors to do so are RGB-D cameras, which can hit limitations in highly dynamic environments due to motion blur and frame-rate constraints. Event cameras have remarkable features such as high temporal resolution and low latency, which make them a potentially ideal vision sensors for object pose tracking at high speed. Even so, there are still only few works on 6D pose tracking with event cameras. In this work, we take advantage of the high temporal resolution and propose a method that uses both a propagation step fused with a pose correction strategy. Specifically, we use 6D object velocity obtained from event-based optical flow for pose propagation, after which, a template-based local pose correction module is utilized for pose correction. Our learning-free method has comparable performance to the state-of-the-art algorithms, and in some cases out performs them for fast-moving objects. The results indicate the potential for using event cameras in highly-dynamic scenarios where the use of deep network approaches are limited by low update rates.