Active scene recognition for programming by demonstration using next-best-view estimates from hierarchical Implicit Shape Models

Meisner, Pascal; Reckling, Reno; Wittenbeck, Valerij; Schmidt-Rohr, Sven R.; Dillmann, Rüdiger

doi:10.1109/icra.2014.6907680

Cited by 4 publications

(5 citation statements)

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“…To this end, we presented 'Active Scene Recognition' (ASR) in [7,8], which is a procedure that integrates the scene recognition and object search. Roughly, the procedure is as follows: The robot first detects some objects and computes which scene categories these objects may belong to.…”

Section: Cereals -On Shelfmentioning

confidence: 99%

“…To make ASR possible, we had to link two research problems: scene recognition and object search. To this end, we proposed a technique for predicting the poses of searched objects in [7] and reused it in [8]. However, this technique suffered from a combinatorial explosion.…”

Section: Contributions and Differences From Previous Workmentioning

confidence: 99%

“…Our approach to creating suitable conditions in spatially distributed and cluttered indoor environments is to have a mobile robot adopt camera views from which it can perceive searched objects. To this end, in two previous works ( [7,8]), we introduced active scene recognition (ASR)-an approach that connects PSR with a three-dimensional object search within a decision-making system. We implemented ASR as a state machine consisting of two search modes (states), DIRECT_SEARCH and INDIRECT_SEARCH, that alternate.…”

Section: State Machine and Next-best-view Optimization-previous Workmentioning

confidence: 99%

“…When using ISM trees, some object poses may need to be predicted using entire sequences of spatial relations. This is prone to a combinatorial explosion: An algorithm presented in our previous work ( [7]) for predicting poses suffered from such an explosion. In the next subsection, we address this problem with an efficient algorithmic solution (one of the contributions of this article).…”

Section: State Machine and Next-best-view Optimization-previous Workmentioning

confidence: 99%

“…The algorithm originating from our previous work ( [7]) was unable to efficiently predict object poses because it processed all of the relative poses that make up a spatial relation in an ISM. Across multiple ISMs, this would lead to a combinatorial explosion: If it generated a prediction for each relative pose in a relation of an ISM m k at tree level k and passed the prediction as a possible pose of a reference object to another ISM m k+1 at level k + 1, each of these would be combined with all the relative poses in a relation of ISM m k+1 .…”

Section: Contribution 3-object Pose Prediction Algorithmmentioning

confidence: 99%

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Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

Meißner,

Dillmann

2023

Robotics

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This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed “Implicit Shape Model (ISM) trees” as a scene model to solve these two tasks together. This article presents novel algorithms for ISM trees to recognize scenes and predict object poses. For us, scenes are sets of objects, some of which are interrelated by 3D spatial relations. Yet, many false positives may occur when using single ISMs to recognize scenes. We developed ISM trees, which is a hierarchical model of multiple interconnected ISMs, to remedy this. In this article, we contribute a recognition algorithm that allows the use of these trees for recognizing scenes. ISM trees should be generated from human demonstrations of object configurations. Since a suitable algorithm was unavailable, we created an algorithm for generating ISM trees. In previous work, we integrated the object search and scene recognition into an active vision approach that we called “Active Scene Recognition”. An efficient algorithm was unavailable to make their integration using predicted object poses effective. Physical experiments in this article show that the new algorithm we have contributed overcomes this problem.

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Section: Cereals -On Shelfmentioning

confidence: 99%

Section: Contributions and Differences From Previous Workmentioning

confidence: 99%

Section: State Machine and Next-best-view Optimization-previous Workmentioning

confidence: 99%

Section: State Machine and Next-best-view Optimization-previous Workmentioning

confidence: 99%

Section: Contribution 3-object Pose Prediction Algorithmmentioning

confidence: 99%

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