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

Eidenberger, Robert; Scharinger, Josef

doi:10.1109/iros.2010.5651927

Cited by 53 publications

(50 citation statements)

References 11 publications

(12 reference statements)

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“…By the total law of probability and the definition of the state history, we obtain (30c) from (30b). Lastly, (30d) holds by the definition of the value function defined in (13). We now assume that the equality in (13) holds for time steps T − 2, T − 3, .…”

Section: Appendixmentioning

confidence: 99%

Unpredictable Planning Under Partial Observability

Hibbard

Savas

Tanaka

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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We study the problem of synthesizing a controller that maximizes the entropy of a partially observable Markov decision process (POMDP) subject to a constraint on the expected total reward. Such a controller minimizes the predictability of a decision-maker's trajectories while guaranteeing the completion of a task expressed by a reward function. First, we prove that a decision-maker with perfect observations can randomize its paths at least as well as a decision-maker with partial observations. Then, focusing on finite-state controllers, we recast the entropy maximization problem as a so-called parameter synthesis problem for a parametric Markov chain (pMC). We show that the maximum entropy of a POMDP is lower bounded by the maximum entropy of this pMC. Finally, we present an algorithm, based on a nonlinear optimization problem, to synthesize an FSC that locally maximizes the entropy of a POMDP over FSCs with the same number of memory states. In numerical examples, we demonstrate the proposed algorithm on motion planning scenarios.

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Section: Appendixmentioning

confidence: 99%

Unpredictable Planning Under Partial Observability

Hibbard

Savas

Tanaka

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…For example, Hager and Wegbreit (2011) demonstrate the utility of considering a prior 3-D scene model and its potential evolution over scenes. Using this observation as a premise, active perception approaches (e.g., Eidenberger and Scharinger (2010); Velez et al (2012); Atanasov et al (2013)) seek the next best view (camera pose) where previously-occluded objects may be visible, typically by formulating the problem as a partially-observable Markov decision process. Because the focus is on planning instead of estimation, this line of work is complementary to the world modeling problem, which considers estimation using measurements from an uncontrolled, arbitrary collection of camera poses.…”

Section: Semantic World Modelingmentioning

confidence: 99%

Data Association for Semantic World Modeling from Partial Views

Wong¹,

Kaelbling²,

Lozano-Pérez³

2016

Springer Tracts in Advanced Robotics

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Autonomous mobile-manipulation robots need to sense and interact with objects to accomplish high-level tasks such as preparing meals and searching for objects. To achieve such tasks, robots need semantic world models, defined as object-based representations of the world involving task-level attributes. In this work, we address the problem of estimating world models from semantic perception modules that provide noisy observations of attributes. Because attribute detections are sparse, ambiguous, and are aggregated across different viewpoints, it is unclear which attribute measurements are produced by the same object, so data association issues are prevalent. We present novel clustering-based approaches to this problem, which are more efficient and require less severe approximations compared to existing tracking-based approaches. These approaches are applied to data containing object type-and-pose detections from multiple viewpoints, and demonstrate comparable quality using a fraction of the computation time.

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“…The work that is closest to ours [27] uses a mobile sensor to classify stationary objects and estimate their poses. Static detection is performed using SIFT matching, and the object pose distributions are represented with Gaussian mixtures.…”

Section: Related Workmentioning

confidence: 99%

Nonmyopic View Planning for Active Object Classification and Pose Estimation

Atanasov

Sankaran

et al. 2014

IEEE Trans. Robot.

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One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing, and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection in which the point of view of a mobile depth camera is controlled. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. Then, a sequence of views, which balances the amount of energy used to move the sensor with the chance of identifying the correct hypothesis, is planned. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate partially observable Markov decision process algorithm. The validity of our approach is verified through simulation and realworld experiments with the PR2 robot. The results suggest that the approach outperforms the widely used greedy viewpoint selection and provides a significant improvement over static object detection.

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

Cited by 53 publications

References 11 publications

Unpredictable Planning Under Partial Observability

Unpredictable Planning Under Partial Observability

Data Association for Semantic World Modeling from Partial Views

Nonmyopic View Planning for Active Object Classification and Pose Estimation

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