Data fusion through fuzzy logic applied to feature extraction from multi-sensory images

Abdulghafour, M.; Chandra, T.; Abidi, Mongi A.

doi:10.1109/robot.1993.292171

Cited by 19 publications

(7 citation statements)

References 6 publications

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“…We can extract one dimensional intensity data, I (8}, and range data, R (8), by omnidirectional sampling. The data of the target location, I, (8} and .R, (8), have to be obtained as a prior information before starting the local homing navigation. On the other hand, the data of the current location, Ic (8} and Rc (8} are obtained at the current location of the robot during the navigation.…”

Section: Omnidirectional Sensor Datamentioning

confidence: 99%

“…Using the proposed method, we can determine a more precise geometric relation between the current location and the target location. To evaluate the proposed environment modeling and to verify the sensor fusion method, experiments are carried out in an indoor environment.Recently various types of sensors have been applied to obtain more accurate data and to improve the intelligence because a single sensor does not provide sufficient information needed for an intelligent system[7], [8]. Such multiple sensors increase the reliability of the sensory information and provide more complete information about the environment by combining partial information from each sensor.…”

mentioning

confidence: 99%

“…Recently various types of sensors have been applied to obtain more accurate data and to improve the intelligence because a single sensor does not provide sufficient information needed for an intelligent system[7], [8]. Such multiple sensors increase the reliability of the sensory information and provide more complete information about the environment by combining partial information from each sensor.…”

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confidence: 99%

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Sensor Fusion for Omnidirectional Sensor-Based Local Homing Navigation using Fuzzy Arithmetic

Bang¹,

Kim²,

Chung³

1998

Intelligent Automation & Soft Computing

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In this paper, we propose a novel environment modeling method for local homing for indoor mobile navigation. An environment model is constructed from a set of omnidirectional sensor data obtained by rotating an ultrasonic sensor and a vision sensor. We develop a sensor fusion method to deal with uncertainties in the two types of sensor data. The proposed sensor fusion method uses fuzzy numbers to represent subjective knowledge and manipulates uncertain quantities using fuzzy arithmetic. Using the proposed method, we can determine a more precise geometric relation between the current location and the target location. To evaluate the proposed environment modeling and to verify the sensor fusion method, experiments are carried out in an indoor environment.Recently various types of sensors have been applied to obtain more accurate data and to improve the intelligence because a single sensor does not provide sufficient information needed for an intelligent system[7], [8]. Such multiple sensors increase the reliability of the sensory information and provide more complete information about the environment by combining partial information from each sensor. But, as various types of sensors are involved, an appropriate sensor fusion method is required to handle the uncertainties effectively. Sensor fusion, as defined in this paper, refers to any stage in the integration process where there is an actual combination( or fusion) of different sources of sensory information into one representational format. The fusion of data or information from multiple sensors or a single sensor over time can take place at different levels of representation[9], [10]. So far, the probabilistic approach has dominated much of the work on the representation and manipulation of uncertainties [ 11]. However, it is difficult to obtain probability density functions for a variety of environments [12]. Furthermore, if we know the probability density functions(p.d.fs), the calculation with p.d.fs is highly complicated [13]. However, when fuzzy arithmetic is used, we can represent the environment using sensor information with more ease and also incorporate the expert's knowledge about the properties of sensors.Inherent uncertainties in the sensory information, which can arise due to noise, changes in the environment, or changes in the system itself, can be represented in different ways. If we use fuzzy numbers, the degree of possibility can be easily represented. When a sensor provides a measured value m about a feature f of an environment, the sensor is actually providing a linguistic proposition of the form of "the feature f is about m". A possibility distribution can be determined to represent the degree of possibility that any x in the referential set X might actually be the true value f [ 14]. In general, the mean value of the fuzzy number represents the actual sensor measurement and the spread of the fuzzy number represents the uncertainty level, which reflects the physical properties of each sensor.

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Section: Omnidirectional Sensor Datamentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Sensor Fusion for Omnidirectional Sensor-Based Local Homing Navigation using Fuzzy Arithmetic

Bang¹,

Kim²,

Chung³

1998

Intelligent Automation & Soft Computing

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“…The application areas for these vision systems are mostly surveillance and safety. [1] Data and help increase decision accuracy. The total amount of data is called Data Fusion Model was originally created for use in the military [2] in planning decisions for the opponent and the location of the enemy.…”

Section: Introductionmentioning

confidence: 99%

Multisensor data fusion model for activity detection

Wichit

2014

2014 Twelfth International Conference on ICT and Knowledge Engineering

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This paper describes a novel system for detecting human activity based on multi-sensor approach. This approach aim to provide accuracy and robustness to the activity recognition system. The performances of the systems that fuse multiple data coming from different sources are deemed to benefit from the heterogeneity and the diversity of the information involved. In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. This applies an information fusion algorithm based Fuzzy Logic with a set of rules in order to recognize Human Behavior. So as to obtain Feature-level Fusion from Extraction of physical data.

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“…It yields more meaningful information as compared to the data obtained from any individual sensor module (Deleroix and Abidi 1988;Mitiche and Aggarwal 1985). Most fusion systems in robotics are intended for applications such as pattern recognition (Russo and Ramponi 1994;Abdulghafour et al 1993) and localization (Kam et al 1997;Manyika and Durrant-Whyte 1994). The proposed techniques for sensor data processing include the least square method, Bayesian method, fuzzy logic, neural network and etc.…”

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confidence: 99%

Intelligent Sensor Fusion and Learning for Autonomous Robot Navigation

Tan

Chen

Wang

et al. 2005

Applied Artificial Intelligence

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& This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.Target collection using autonomous robots can be broadly applied to a variety of fields such as product transferring in manufacturing factory, rubbish cleaning in office, and bomb searching on battle field, etc. Such robots should be able to cope with the large amount of uncertainties existing in physical environment which is often dynamic and unpredictable. The task requires the robot to achieve certain goals without bumping into obstacles based on sensor readings. However, the goal-based world model is often difficult to be precisely predefined and sometimes the obstacles are not static. Moreover, the sensor information is imprecise and unreliable in most cases. Therefore, the autonomous robot controller needs to be endowed with the

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Data fusion through fuzzy logic applied to feature extraction from multi-sensory images

Cited by 19 publications

References 6 publications

Sensor Fusion for Omnidirectional Sensor-Based Local Homing Navigation using Fuzzy Arithmetic

Sensor Fusion for Omnidirectional Sensor-Based Local Homing Navigation using Fuzzy Arithmetic

Multisensor data fusion model for activity detection

Intelligent Sensor Fusion and Learning for Autonomous Robot Navigation

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