用事件相机实现高速6维物体姿态跟踪，无需深度学习。
Event-based Motion & Appearance Fusion for 6D Object Pose Tracking
- 基于事件流光流估计6维运动速度，用于姿态传播。
- 通过模板匹配实现局部姿态校正，提升精度。
- 适合高速动态场景，优于传统方法在快速运动时的表现。
物体姿态跟踪是机器人在家庭和工业环境中执行任务的基础任务。目前最常用的传感器是RGB-D相机，但在高度动态环境中易受运动模糊和帧率限制。事件相机具有高时间分辨率和低延迟的特性，是高速姿态跟踪的理想视觉传感器。然而，针对事件相机的6维姿态跟踪研究仍较少。本文提出一种利用高时间分辨率的方法，结合姿态传播与姿态校正策略：首先通过事件光流获得6维物体速度进行姿态传播，随后使用基于模板的局部姿态校正模块进行优化。该方法无需训练，性能可媲美现有先进算法，在某些情况下对快速运动物体表现更优。结果表明，事件相机在低更新率限制下仍具潜力，适用于高速动态场景。
原文摘要 · Abstract (English)
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.
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