Real-Time Human Pose Tracking from Range Data

Ganapathi, Varun; Plagemann, Christian; Koller, Daphne; Thrun, Sebastian

doi:10.1007/978-3-642-33783-3_53

Cited by 116 publications

(125 citation statements)

References 22 publications

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“…As pointed out by Ganapathi, et al [3], for each point pt i in the point cloud, we know that there must be clear line-of-sight between the camera origin and that point. Thus, we know that no surface on the proposed model can lie between that point and the camera.…”

Section: Measurement Model For Point Clouds: Free Spacementioning

confidence: 92%

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Tracking objects with point clouds from vision and touch

Izatt

Mirano

Adelson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified secondorder update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot's end effector.

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Section: Measurement Model For Point Clouds: Free Spacementioning

confidence: 92%

“…Taking inspiration from Ganapathi et al [3], we compute DF obs and find this closest point efficiently by decomposing DF obs into components perpendicular and parallel to the camera view ray. This decomposition allows us to avoid computing the full 3D distance function to S obs .…”

Section: A Approximate Minimization Of Sdf (Ptmentioning

confidence: 99%

Tracking objects with point clouds from vision and touch

Izatt

Mirano

Adelson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…In contrast, our framework introduces articulated and symmetric signed distance functions to solve the data association problem robustly and efficiently, enabling real-time tracking using all input data points. Another approach similar to ours is that of Ganapathi et al [12], which uses range data to track articulated motion of human bodies by augmenting traditional ICP with free space information. Our work differs by incorporating information about observed free space directly into the energy function, rather than using constraint projection.…”

Section: Related Workmentioning

confidence: 99%

DART: Dense Articulated Real-Time Tracking

Schmidt

Newcombe

Fox

2014

Robotics: Science and Systems X

136

130

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Abstract-This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree. DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators. To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization. We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past. I. INTRODUCTIONThe ability to accurately track the pose of objects in real time is of fundamental importance to many areas of robotics. Applications range from navigation to planning, manipulation and human-robot interaction, all of which have received the attention of researchers working within a state-space modelbased paradigm within both computer vision and robotics. The class of objects that can be described as collections of rigid bodies chained together through a kinematic tree is quite broad, including furniture, tools, human bodies, human hands, and robot manipulators. Tracking articulated bodies from a single viewpoint and without instrumenting the object of interest still presents a significant challenge where the single viewpoint and occlusions, including self-occlusion, limit the amount of information available for pose estimation. Noisy sensor data and approximate object models pose additional problems. Finally, the objects being tracked can be highly dynamic and have many degrees of freedom, making real-time tracking difficult.Early articulated model-based tracking techniques relied on tracking 2D features such as image edges on a CPU [8,4]. Recently introduced depth cameras along with highly parallel algorithms optimized for modern GPUs have enabled new algorithms for tracking complex 3D objects in real time. Examples include KinectFusion and related efforts for 3D mapping [23,16,34], human body pose tracking [29,35,15], articulated hand tracking [24,19,26]. These approaches were developed for specific application domains and have not been demonstrated or tested on multiple tracking applications. The application-specific nature of these approaches enables their authors to show excellent performance by taking advantage of domain-specific features and constraints, but it also prevents them from serving as general tools for tracking arbitrary articulated objects. Techniques have also been developed to

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“…The body model is fitted to depth data or to a combination of depth and image features [5,8]. [2] propose 1 http://resources.mpi-inf.mpg.de/InertialDepthTracker a generative depth-based tracker using a modified energy function that incorporates empty space information, as well as inter-penetration constraints. An approach that uses multiple depth cameras for pose estimation which reduces the occlusion problem is presented in [19].…”

Section: Related Workmentioning

confidence: 99%