Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented.

Multisensor fusion and integration: approaches, applications, and future research directions

A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Architectures of sensor networks for biological and chemical agent detection and identification

In this paper, software architecture for multisensor fusion system (MFS) in dynamic environment is described. The architecture has the characteristics of robustness, dynamically reconfigurable framework, "plug-in-play" for sensor, and predictability of sensor action. According to requirements of MFS, the real-time operating system (RTOS) is applied in this software architecture. The result is an RTOS-based software architecture, which consists of five kinds of tasks: sensor sampling task (SST), sensor data processing task (SDPT), fusion task (FT), management task (MT) and man/machine interface task (MMIT). A calligraphic robot has been designed and developed in our lab based on the software architecture proposed. The application of the architecture makes the sensor management of the calligraphic robot system easy.

RTOS-based software architecture for multisensor fusion system

In this paper, a general software architecture for multisensor fusion system (MFS) in dynamic environment is described. The architecture has the characteristics of robustness, dynamically reconfigurable framework, "plug-in-play" for sensor, and the predictability of sensor action. According to requirements of MFS, the real-time operating system (RTOS) is applied in this software architecture. The result is an RTOS-based software architecture, which consists of five kinds of tasks: sensor sampling task (SST), sensor data processing task (SDPT), fusion task (FT), management task (MT) and man/machine interface task (MMIT). A calligraphic robot has been designed and developed in our lab based on the software architecture proposed.

General software architecture for multi-sensor information fusion system

Intelligent mobile robots, the ultimate goal for researchers in the mobile robotics area, can be defined as robots that have such capabilities as perception, reasoning, decision making, action and learning. As a research work toward this ultimate goal, in this thesis, two major fields for intelligent mobile robot have been studied: environment recognition and navigation algorithm. The study is based on indoor wheeled mobile robot that moves within structured but unknown 2D environment, and the mobile robot LCAR has been used as a test bed. With the aim of more robust environment recognition, a sensor fusion scheme for mobile robot environment recognition that incorporates range data and contour data is proposed. Ultrasonic sensor provides coarse spatial description but guarantees open space (with no obstacle) within sonic cone with relatively high belief. Laser structured light system provides detailed contour description of environment but prone to light noise and is easily affected by surface reflectivity. A sensor fusion scheme that can compensate the disadvantages of both sensors has been proposed. Scanning of environments is performed by rotating two sensors installed on the pan-tilt device on the top of the robot. For the purpose of not losing spatial detail and more natural environment description, line and corner models are used. Overall fusion process is composed of two stages: Noise elimination and belief updates. Dempster-Shafer’s evidential reasoning is applied at each stage. Open space estimation from sonar range measurements brings elimination of noisy lines from laser sensor. Comparing actual sonar data to the simulated

Environment recognition with multiple sensors and mobile robot navigation based on the fuzzy via-point selection method = 다중 센서를 이용한 환경인식 및 퍼지 경유점 선택기법에 의한 주행 알고리즘

The need for intelligent systems that can operate in an unstructured dynamic environment has created a growing demand for the use of multiple, distributed sensors. While most research in multisensor fusion has revolved around applications in object recognition-including military applications for automatic target recognition-developments in microsensor technology are encouraging more research in affordable, highly-redundant sensor networks. Three trends that are described at length are the increasing use of microsensors, the techniques that are used in the handling of partial or uncertain data, and the application of neural network techniques for sensor fusion. >

W.G. Lee

Papers

Future trends in multisensor integration and fusion