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Real-Time Human Pose Tracking from Range Data
Varun Ganapathi
Daphne Koller
Sebastian Thrun
Proceedings of the European Conference on Computer Vision (ECCV) (2012)
Preview abstract
Tracking human pose in real-time is a difficult problem with many interesting applications. Existing solutions suffer from a variety of problems, especially when confronted with unusual human poses. In this paper, we derive an algorithm for tracking human pose in real-time from depth sequences based on MAP inference in a probabilistic temporal model. The key idea is to extend the iterative closest points (ICP) objective by modeling the constraint that the observed subject cannot enter free space, the area of space in front of the true range measurements. Our primary contribution is an extension to the articulated ICP algorithm that can efficiently enforce this constraint. Our experiments show that including this term improves tracking accuracy significantly. The resulting filter runs at 125 frames per second using a single desktop CPU core. We provide extensive experimental results on challenging real-world data, which show that the algorithm outperforms the previous state-of the-art trackers both in computational efficiency and accuracy.
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Body Schema Learning
Jürgen Sturm
Wolfram Burgard
Towards Service Robots for Everyday Environments (2012), pp. 131-161
Assisted Highway Lane Changing with RASCL
Richard Oliver Frankel
Olafur Gudmundsson
Brett Miller
Jordan Potter
Todd Sullivan
Salik Syed
Doreen Hoang
Jae Min John
Ki-Shui Liao
Pasha Nahass
Amanda Schwab
Jessica Yuan
David Stavens
Clifford Nass
Sebastian Thrun
AAAI Spring Symposium: Embedded Reasoning (2010)
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Cyrill Stachniss
Jürgen Hess
Felix Endres
Nathan Franklin
Robotics and Autonomous Systems, vol. 58 (2010), pp. 762-772
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J. Zico Kolter
David T. Jackson
Andrew Y. Ng
Sebastian Thrun
ICRA (2010), pp. 839-845
Real-time identification and localization of body parts from depth images
Unsupervised discovery of object classes from range data using latent Dirichlet allocation
Felix Endres
Cyrill Stachniss
Wolfram Burgard
Robotics: Science and Systems (2009)
Look-ahead Proposals for Robust Grid-based SLAM with Rao-Blackwellized Particle Filters
Slawomir Grzonka
Giorgio Grisetti
Wolfram Burgard
I. J. Robotic Res., vol. 28 (2009), pp. 191-200
Modeling RFID signal strength and tag detection for localization and mapping
A Bayesian regression approach to terrain mapping and an application to legged robot locomotion
Sebastian Mischke
Sam Prentice
Kristian Kersting
Nicholas Roy
Wolfram Burgard
J. Field Robotics, vol. 26 (2009), pp. 789-811
Probabilistic situation recognition for vehicular traffic scenarios
Classifying dynamic objects
Matthias Luber
Kai Oliver Arras
Wolfram Burgard
Auton. Robots, vol. 26 (2009), pp. 141-151
Learning Kinematic Models for Articulated Objects
Jürgen Sturm
Vijay Pradeep
Cyrill Stachniss
Kurt Konolige
Wolfram Burgard
IJCAI (2009), pp. 1851-1856
Learning gas distribution models using sparse Gaussian process mixtures
Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness
Learning predictive terrain models for legged robot locomotion
Sebastian Mischke
Sam Prentice
Kristian Kersting
Nicholas Roy
Wolfram Burgard
IROS (2008), pp. 3545-3552
Adaptive Body Scheme Models for Robust Robotic Manipulation
Estimating landmark locations from geo-referenced photographs
Unsupervised body scheme learning through self-perception
Efficiently learning high-dimensional observation models for Monte-Carlo localization using Gaussian mixtures
Monocular range sensing: A non-parametric learning approach
Felix Endres
Juergen Michael Hess
Cyrill Stachniss
Wolfram Burgard
ICRA (2008), pp. 929-934
Gas Distribution Modeling using Sparse Gaussian Process Mixture Models
Cyrill Stachniss
Achim J. Lilienthal
Wolfram Burgard
Robotics: Science and Systems (2008)
Classifying Dynamic Objects: An Unsupervised Learning Approach
Matthias Luber
Kai Oliver Arras
Wolfram Burgard
Robotics: Science and Systems (2008)
Gaussian mixture models for probabilistic localization
Most likely heteroscedastic Gaussian process regression
Efficient Failure Detection on Mobile Robots Using Particle Filters with Gaussian Process Proposals
A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems
Daniel Meyer-Delius
Georg von Wichert
Wendelin Feiten
Gisbert Lawitzky
Wolfram Burgard
GfKl (2007), pp. 269-276
Improved likelihood models for probabilistic localization based on range scans
Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders
Kristian Kersting
Patrick Pfaff
Wolfram Burgard
Robotics: Science and Systems (2007)
Adaptive Non-Stationary Kernel Regression for Terrain Modeling
Autonomous blimp control using model-free reinforcement learning in a continuous state and action space
Look-Ahead Proposals for Robust Grid-Based SLAM
Efficient Failure Detection for Mobile Robots Using Mixed-Abstraction Particle Filters
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Alexandru Cocora
Kristian Kersting
Wolfram Burgard
Luc De Raedt
IROS (2006), pp. 2792-2797
Sequential Parameter Estimation for Fault Diagnosis in Mobile Robots Using Particle Filters
Vision-Based 3D Object Localization Using Probabilistic Models of Appearance