Abstract
Estimating motion from images is a well-studied problem in computer vision and robotics. Previous work has developed techniques to estimate the motion of a moving camera in a largely static environment (e.g., visual odometry) and to segment or track motions in a dynamic scene using known camera motions (e.g., multiple object tracking). It is more challenging to estimate the unknown motion of the camera and the dynamic scene simultaneously. Most previous work requires a priori object models (e.g., tracking-by-detection), motion constraints (e.g., planar motion), or fails to estimate the full SE(3) motions of the scene (e.g., scene flow). While these approaches work well in specific application domains, they are not generalizable to unconstrained motions. This paper extends the traditional visual odometry (VO) pipeline to estimate the full SE(3) motion of both a stereo/RGB-D camera and the dynamic scene. This multimotion visual odometry (MVO) pipeline requires no a priori knowledge of the environment or the dynamic objects. Its performance is evaluated on a real-world dynamic dataset with ground truth for all motions from a motion capture system.
Abstract (translated)
估计图像的运动是计算机视觉和机器人技术中一个经过充分研究的问题。先前的工作已经开发了用于在大部分静态环境（例如，视觉测距）中估计移动相机的运动并使用已知的相机运动（例如，多个对象跟踪）来分割或跟踪动态场景中的运动的技术。同时估计摄像机的未知运动和动态场景更具挑战性。大多数先前的工作需要先验对象模型（例如，逐个检测），运动约束（例如，平面运动），或者不能估计场景的完整SE（3）运动（例如，场景流）。虽然这些方法在特定的应用领域中运行良好，但它们并不适用于不受约束的运动。本文扩展了传统的视觉测距（VO）管道，以估计立体/ RGB-D摄像机和动态场景的完整SE（3）运动。这种多运动视觉测距（MVO）管道不需要环境或动态对象的先验知识。其性能在真实动态数据集上进行评估，其中包含来自运动捕捉系统的所有运动的基础事实。
URL
https://arxiv.org/abs/1808.00274
