Multisensory scene interpretation: model-based object recognition

Grandjean, P.; Ghallab, Malik; Dekneuvel, Eric

doi:10.1109/robot.1991.131844

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…These hypothetical 3D landmarks are inserted into the DLM. In contrast to common prediction of 2D sensor readings (see [5], [lo]) this is done independent of their visibility from the current position of the robot. This allows the verification of the hypothetical features, equivalent to the verification of the object hypotheses, from different points of viewsexploiting the advantages of the robot's motion with reduced drawbacks.…”

Section: The Predictive Spatial Completionmentioning

confidence: 99%

Vision based model generation for indoor environments

Burschka

Eberst²,

Robl³

Proceedings of International Conference on Robotics and Automation

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This paper presents our approach to retrieve a dependable three-dimensional description of a partially known indoor environment. W e describe the way the sensor data f r o m a video camera is preprocessed b y contour tracing to extract the boundary lines of the objects and how this information is transformed into a three-dimensional environmental model of the world. W e introduce a dynamic map that operates in a closed loop with various sensor systems improving their performance by filtering and contributing certain knowledge. The filtering relies on the capability of a mobile robot to gather sensor readings f r o m different positions. A n important part of our approach is the interaction between the dynamic map, storing and filtering the incoming information, and a module predicting missing sensor features based on structures andreference objects. This interaction helps to generate a more accurate model containing also poor detectable features, that are impossible to extract from a single sensor view. 0-7803-3612-7-4/97 $5.00 0 1997 IEEE

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Section: The Predictive Spatial Completionmentioning

confidence: 99%

Vision based model generation for indoor environments

Burschka

Eberst²,

Robl³

Proceedings of International Conference on Robotics and Automation

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“…This is because sensor planning can provide the extension of sensing capability as well as the enhancement of accuracy and efficiency in sensin . However, most work on sensor planning currently availabfe are based on ad hoc methods customized to articular problems, lackin formality and genesalit in more, it is yet to be explored how to accompish sensor Elanning in an environment where model-and knowledgeased sensing [5] as well as redundant sensing[ 101 are allowed.…”

Section: Introductionmentioning

confidence: 99%

Sensor planning with hierarchically distributed perception net

Lee

Zhao²

Proceedings of 1995 IEEE International Conference on Robotics and Automation

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A new paradigm of sensor planning is presented based on a Hierarchically Distributed Perception Net (HDP.N) proposed as a general sensing architecture. In the proposed parametric sensor planning, the uncertainties are propagated in HDPN, and the sensing parameters of HDPN are iteratively modijied so that HDPN ultimately generates the desired accuracy of outputs at a minimum sensing cost. The structural sensor planning aims at self-organizing an optimal conJiguration of HDPN by exploiting redundant sensing. Simulation study and experiment are conducted by applying the proposed parametric sensor planning method for the accurate self-localization of a mobile robot operating in a known environment with multiple range sensors. The proposed paradigm provides a formal, yet general and eficient, method of representing and solving a sensor planning problem for an integrated sensor system.

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New approach for aggregating multi‐sensory data

Basir

Shen

1993

J. Robotic Syst.

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The task of sensory data fusion may involve the aggregation of sensory measurements that may be from different phenomenological domains and that, in many cases, could embrace some conflicting information cues. It is rather a challenge to find suitable strategies by which measurements obtained by the different sensors of the system can be aggregated so that a consistent interpretation of these measurements is achieved. In this article, we present a novel approach to achieve this goal. A recursive group utility function that is capable of bringing the group of sensors into consensus is used. After each sensor in the group gathers information relevant to the sensory task, the group engages in what we call the uncertainty estimation stage. This is an information theorybased process that allows each sensor to assess its self‐uncertainty and the conditional uncertainties of other sensors. This process facilitates the computation of a weighting scheme that operates recursively on sensor observations until the group reaches a consensus. Whenever new observations are made, the uncertainty estimates of sensors are updated and used to compute a new weighting scheme. To demonstrate the efficacy and to show how the methodology works, the article discusses how the method can be used to tackle the multi‐sensor object identification problem. © 1993 John Wiley & Sons, Inc.

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Multisensory scene interpretation: model-based object recognition

Cited by 11 publications

References 11 publications

Vision based model generation for indoor environments

Vision based model generation for indoor environments

Sensor planning with hierarchically distributed perception net

New approach for aggregating multi‐sensory data

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