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

ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings