  In this paper, we tackle the problem of multibody SLAM from a monocular
camera. The term multibody, implies that we track the motion of the camera, as
well as that of other dynamic participants in the scene. The quintessential
challenge in dynamic scenes is unobservability: it is not possible to
unambiguously triangulate a moving object from a moving monocular camera.
Existing approaches solve restricted variants of the problem, but the solutions
suffer relative scale ambiguity (i.e., a family of infinitely many solutions
exist for each pair of motions in the scene). We solve this rather intractable
problem by leveraging single-view metrology, advances in deep learning, and
category-level shape estimation. We propose a multi pose-graph optimization
formulation, to resolve the relative and absolute scale factor ambiguities
involved. This optimization helps us reduce the average error in trajectories
of multiple bodies over real-world datasets, such as KITTI. To the best of our
knowledge, our method is the first practical monocular multi-body SLAM system
to perform dynamic multi-object and ego localization in a unified framework in
metric scale.
