Probabilistic Rule Set Joint State Update as approximation to the full joint state estimation applied to multi object scene analysis

Grundmann, Thilo; Fiegert, Michael; Burgard, Wolfram

doi:10.1109/iros.2010.5650433

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…the observation model parameters with the sample mean and covariance as before, but instead of performing local optimization, we simply generate samples from this joint Gaussian distribution and accept them if they are feasible. This is similar to what a naïve particle filter would do, where the feasibility check performs a 'hard' version of the physics simulation-based rule set in [10]. We then use the sample that has the greatest likelihood as the revised state estimate (i.e., it performs the best out of all samples on the optimization problem).…”

Section: Resultsmentioning

confidence: 99%

“…However, they suffer from the curse of dimensionality and become computationally intractable in high dimensions. To mitigate this, Grundmann et al [10] used a particle filter that first updated object states separately, then evaluated the joint estimates using a rule set. The rule set encoded physical constraints such as non-interpenetration and gravity, and was evaluated using a physics simulator.…”

Section: A Related Workmentioning

confidence: 99%

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Collision-free state estimation

Wong

Kaelbling

Lozano-Pérez

2012

2012 IEEE International Conference on Robotics and Automation

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In state estimation, we often want the maximum likelihood estimate of the current state. For the commonly used joint multivariate Gaussian distribution over the state space, this can be efficiently found using a Kalman filter. However, in complex environments the state space is often highly constrained. For example, for objects within a refrigerator, they cannot interpenetrate each other or the refrigerator walls. The multivariate Gaussian is unconstrained over the state space and cannot incorporate these constraints. In particular, the state estimate returned by the unconstrained distribution may itself be infeasible. Instead, we solve a related constrained optimization problem to find a good feasible state estimate. We illustrate this for estimating collision-free configurations for objects resting stably on a 2-D surface, and demonstrate its utility in a real robot perception domain.

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Section: Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Collision-free state estimation

Wong

Kaelbling

Lozano-Pérez

2012

2012 IEEE International Conference on Robotics and Automation

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“…al. [32] proposed a method to increase the estimation accuracy of independent sub-state estimation using statistical dependencies in the prior. The dependencies in the prior are modeled by physical relations.…”

Section: Related Workmentioning

confidence: 99%

Table-top scene analysis using knowledge-supervised MCMC

Liu

Chen

Wurm

et al. 2015

Robotics and Computer-Integrated Manufacturing

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models Semantic modelling a b s t r a c tIn this paper, we propose a probabilistic approach to generate abstract scene graphs from uncertain 6D pose estimates. We focus on generating a semantic understanding of the perceived scenes that well explains the composition of the scene and the inter-object relations. The proposed system is realized by our knowledge-supervised MCMC sampling technique. We explicitly make use of task-specific context knowledge by encoding this knowledge as descriptive rules in Markov logic networks. We use a probabilistic sensor model to encode the fact that measurements are subject to significant uncertainty. We integrate the measurements with the abstract scene graph in a data driven MCMC process. Our system is fully probabilistic and links the high-level abstract scene description to uncertain low level measurements. Moreover, false estimates of the object poses and hidden objects of the perceived scenes can be systematically detected using the defined Markov logic knowledge base. The effectiveness of our approach is demonstrated and evaluated in real world experiments.

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“…The method determines object and pose instances according to a global optimization paradigm by minimizing a cost function which encompasses geometrical cues. Grundmann et al [12] propose a probabilistic approach, Rule Set Joint State Update (RSJSU), to estimate the poses of a set of objects simultaneously using the full joint posterior. They assume independence between prior belief, measurement and prediction models to approximate the full state.…”

Section: Related Workmentioning

confidence: 99%

A principle of minimum translation search approach for object pose refinement

Mojtahedzadeh

Lilienthal

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The state-of-the-art object pose estimation approaches represent the set of detected poses together with corresponding uncertainty. The inaccurate noisy poses may result in a configuration of overlapping objects especially in cluttered environments. Under a rigid body assumption the inter-penetrations between pairs of objects are geometrically inconsistent. In this paper, we propose the principle of minimum translation search, PROMTS, to find an inter-penetrationfree configuration of the initially detected objects. The target application is to automate the task of unloading shipping containers, where a geometrically consistent configuration of objects is required for high level reasoning and manipulation. We find that the proposed approach to resolve geometrical inconsistencies improves the overall pose estimation accuracy. We examine the utility of two selected search methods: A-star and Depth-Limited search. The performance of the search algorithms are tested on data sets generated in simulation and from real-world scenarios. The results show overall improvement of the estimated poses and suggest that depth-limited search presents the best overall performance.

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Probabilistic Rule Set Joint State Update as approximation to the full joint state estimation applied to multi object scene analysis

Cited by 10 publications

References 8 publications

Collision-free state estimation

Collision-free state estimation

Table-top scene analysis using knowledge-supervised MCMC

A principle of minimum translation search approach for object pose refinement

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