Probabilistic action planning for active scene modeling in continuous high-dimensional domains

Eidenberger, Robert; Grundmann, Thilo; Zoellner, Raoul

doi:10.1109/robot.2009.5152598

Cited by 15 publications

(15 citation statements)

References 8 publications

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“…The probabilistic planning concept is realized in form of a partially observable Markov decision process, as proposed in [13]. The probabilistic planner reasons by considering information theoretic quality criteria of the expected belief distribution b Ot(at) t (q ), which is abbreviated by b in the following equations, and control action costs r at (b ).…”

Section: B Observation Modelmentioning

confidence: 99%

Active perception and scene modeling by planning with probabilistic 6D object poses

Eidenberger

Scharinger

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents an approach to probabilistic active perception planning for scene modeling in cluttered and realistic environments. When dealing with complex, multiobject scenes with arbitrary object positions, the estimation of 6D poses including their expected uncertainties is essential. The scene model keeps track of the probabilistic object hypotheses over several sequencing sensing actions to represent the real object constellation.To improve detection results and to tackle occlusion problems a method for active planning is proposed which reasons about model and state transition uncertainties in continuous and highdimensional domains. Information theoretic quality criteria are used for sequential decision making to evaluate probability distributions. The probabilistic planner is realized as a partially observable Markov decision process (POMDP).The active perception system for autonomous service robots is evaluated in experiments in a kitchen environment. In 80 test runs the efficiency and satisfactory behavior of the proposed methodology is shown in comparison to a random and a stepaside action selection strategy. The objects are selected from a large database consisting of 100 different household items.

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Section: B Observation Modelmentioning

confidence: 99%

Active perception and scene modeling by planning with probabilistic 6D object poses

Eidenberger

Scharinger

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…This work uses the Bayesian state estimator introduced in [2] and considers uncertainties in the state transition and in the measurement for state estimation. We state the probability distribution over the state…”

Section: B State Estimation and Data Associationmentioning

confidence: 99%

“…The probabilistic planning concept in form of a partially observable Markov decision process, as proposed in [2], is used for finding optimal action policies. Due to realtime constraints and the fact that performing an observation usually greatly influences the beliefs and makes proposed policies obsolete, this concept is slightly simplified to the 1-horizon planning strategy:…”

Section: Perception Planningmentioning

confidence: 99%

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Probabilistic occlusion estimation in cluttered environments for active perception planning

Eidenberger

Zoellner

Scharinger

2009

2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Abstract-This paper presents a probabilistic framework for scene modeling and active perception planning in complex environments. It tackles the problems of representing detection and transition uncertainties in multi-object scenes without knowledge of the total number of objects in the scenario. The correct association of observation data with scene information is essential for reasonable incorporation of sequencing measurements into the scene model. This work also deals with the probabilistic computation of object occlusions for probabilistic action planning in order to select the most profitable prospective viewpoint. Concepts from computer graphics and statistics are combined for the efficient and precise estimation of future observations.In an experimental setting this active perception system, integrated into an autonomous service robot, is evaluated in a kitchen scenario.

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“…1. It is the basis for various other research in the area of object manipulation [3] [4], probabilistic perception planning [5] and scene analysis [6].…”

Section: Introductionmentioning

confidence: 99%

A Gaussian measurement model for local interest point based 6 DOF pose estimation

Grundmann

Feiten

Wichert

2011

2011 IEEE International Conference on Robotics and Automation

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One of the main challenges for service robots during operation lies in the handling of unavoidable uncertainties which originate from model and sensor inaccuracies and which are characteristic for realistic application scenarios. Robustness under real world conditions can only be achieved when the dominant uncertainties are explicitly represented and purposefully managed by the robot's control system. We therefore adopt a probabilistic approach in which perception is regarded as a sequential estimation process and follow a Bayesian filtering methodology. Under these assumptions probabilistic models of the robot's perception systems are key.In this paper we shortly describe a model based object recognition and localization system. However, we do not not focus on the 6D pose estimation procedure itself, but on the method to quantify and compute the uncertainty associated with it. We construct a Gaussian approximation of the resulting pose error using the implicit function theorem. It is then used as a proposal density for importance sampling. Our goal is to sample from the measurement model describing 6D object localization based on local features in a Bayesian filtering context.

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Probabilistic action planning for active scene modeling in continuous high-dimensional domains

Cited by 15 publications

References 8 publications

Active perception and scene modeling by planning with probabilistic 6D object poses

Active perception and scene modeling by planning with probabilistic 6D object poses

Probabilistic occlusion estimation in cluttered environments for active perception planning

A Gaussian measurement model for local interest point based 6 DOF pose estimation

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