Estimating dynamics on-the-fly using monocular video for vision-based robotics

Estimating the physical parameters of articulated multibody systems (AMBSs) using an uncalibrated monocular camera poses significant challenges for vision-based robotics. Articulated multibody models, especially ones including dynamics, have shown good performance for pose tracking, but require good estimates of system parameters. In this paper, we first propose a technique for estimating parameters of a dynamically equivalent model (kinematic/geometric lengths as well as mass, inertia, damping coefficients) given only the underlying articulated model topology. The estimated dynamically equivalent model is then employed to help predict/filter/gap-fill the raw pose estimates, using an unscented Kalman filter. The framework is tested initially on videos of a relatively simple AMBS (double pendulum in a structured laboratory environment). The double pendulum not only served as a surrogate model for the human lower limb in flight phase, but also helped evaluate the role of model fidelity. The treatment is then extended to realize physically plausible pose-estimates of human lower-limb motions, in more-complex uncalibrated monocular videos (from the publicly available DARPA Mind's Eye Year 1 corpus). Beyond the immediate problem-at-hand, the presented work has applications in creation of low-order surrogate computational dynamics models for analysis, control, and tracking of many other articulated multibody robotic systems (e.g., manipulators, humanoids) using vision.