Fast parametric viewpoint estimation for active object detection

Eidenberger, Robert; Grundmann, Thilo; Feiten, Wendelin; Zoellner, Raoul

doi:10.1109/mfi.2008.4648083

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…The decisions were based on Bayesian state estimation and the robots took certain actions to reduce the uncertainties measured by information theory. In [20], a fast method for viewpoint selection and active object recognition based on a sequential Bayesian framework was proposed. They used Bayesian state estimation to update the current state probability distribution based on a scene observation which depends on the sensor parameters.…”

Section: Related Workmentioning

confidence: 99%

Active view planing for human observation through a RGB-D camera

Sheng

2013

2013 IEEE Workshop on Robot Vision (WORV)

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Human sensing is always an important topic for robotic applications. In this paper, we proposed an active view planning approach for human observation on a mobile robot platform with sensor data processing. The sensor adopted in our research is an inexpensive RGB-D camera. A new measure based on distance and orientation information is introduced to evaluate the quality of the viewpoint when the robot detects the human subject. The result shows that the robot can move to the best viewpoint based on the proposed approach. 114 978-1-4673-5647-3/12/$31.00 ©2012 IEEE 114 978-1-4673-5648-0/13/$31.00 ©2013 IEEE

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Section: Related Workmentioning

confidence: 99%

Active view planing for human observation through a RGB-D camera

Sheng

2013

2013 IEEE Workshop on Robot Vision (WORV)

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“…al. [9] presented a sequential Bayesian method for active object recognition based on a cost metric defined as the upper bound of the differential entropy. While their use of Gaussian mixture model approximations to prior and posterior distributions of the state variable allows for fast parametric updates, 6D pose estimation and object tracking capabilities are not explored.…”

Section: Related Workmentioning

confidence: 99%

Dynamic sensor planning with stereo for model identification on a mobile platform

Burdick

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents an approach to sensor planning for simultaneous pose estimation and model identification of a moving object using a stereo camera sensor mounted on a mobile base. For a given database of object models, we consider the problem of identifying an object known to belong to the database and where to move next should the object not be easily identifiable from the initial viewpoint. No constraints on the motion of the object nor the robot itself are assumed, which is an improvement on previous methods. Sensor planning is based on the selection of the control action that optimizes a cost metric based on information gain. Experimental results from the implementation of the method on a two-wheeled nonholonomic robot are presented to illustrate and validate the method.

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“…While we also employ Monte Carlo sampling, computational complexity problems are avoided via a set of approximations. More recently, the authors in [8] present a sequential Bayesian method for active object recognition using an upper bound on the differential entropy as a cost metric. While their use of Gaussian mixture model approximations to prior and posterior distributions of the state variable allows for fast parametric updates, 3D pose estimation and object tracking capabilities are not explored.…”

Section: Related Workmentioning

confidence: 99%

Sensor planning for object pose estimation and identification

Burdick

2009

2009 IEEE International Workshop on Robotic and Sensors Environments

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This paper proposes a novel approach to sensor planning for simultaneous object identification and 3D pose estimation. We consider the problem of determining the next-best-view for a movable sensor (or an autonomous agent) to identify an unknown object from among a database of known object models. We use an information theoretic approach to define a metric (based on the difference between the current and expected model entropy) that guides the selection of the optimal control action. We present a generalized algorithm that can be used in sensor planning for object identification and pose estimation. Experimental results are also presented to validate the proposed algorithm.

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Fast parametric viewpoint estimation for active object detection

Cited by 8 publications

References 15 publications

Active view planing for human observation through a RGB-D camera

Active view planing for human observation through a RGB-D camera

Dynamic sensor planning with stereo for model identification on a mobile platform

Sensor planning for object pose estimation and identification

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