We present ClusterVO, a stereo Visual Odometry which simultaneously clusters
and estimates the motion of both ego and surrounding rigid clusters/objects.
Unlike previous solutions relying on batch input or imposing priors on scene
structure or dynamic object models, ClusterVO is online, general and thus can
be used in various scenarios including indoor scene understanding and
autonomous driving. At the core of our system lies a multi-level probabilistic
association mechanism and a heterogeneous Conditional Random Field (CRF)
clustering approach combining semantic, spatial and motion information to
jointly infer cluster segmentations online for every frame. The poses of camera
and dynamic objects are instantly solved through a sliding-window optimization.
Our system is evaluated on Oxford Multimotion and KITTI dataset both
quantitatively and qualitatively, reaching comparable results to
state-of-the-art solutions on both odometry and dynamic trajectory recovery.

ClusterVO 是一种在线立体视觉里程计，能够同时聚类估计自身和周围的刚性聚类 / 物体的运动，其核心是多级概率相关机制和结合语义、空间和运动信息的异构条件随机场聚类方法，在 Oxford 多动态和 KITTI 数据集上达到了与最先进解决方案相当的结果。