NDLI: Estimating Dynamics On-the-Fly Using Monocular Video For Vision-Based Robotics

Content Provider	IEEE Xplore Digital Library
Author	Agarwal, P. Kumar, S. Ryde, J. Corso, J.J. Krovi, V.N.
Copyright Year	1996
Abstract	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.
Starting Page	1412
Ending Page	1423
Page Count	12
File Size	2011346
File Format	PDF
ISSN	10834435
Volume Number	19
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque Friction Cameras Joints Estimation Robots Dynamics system identification Articulated multibody dynamics estimation monocular video pose estimation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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