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

By Ren-Chyuan Luo, Chih-Chen Yih, Kuo Lan Su. 2002

621 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/077bcdeb78222ada4bbc2a757cd293e3410f22d5

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In Control and Systems Engineering

Citations in the corpus

Multimodal fusion for multimedia analysis: a survey (2010, methodology): “Existing surveys in this direction are mostly focused on a particular aspect of the analysis task, such as multimodal video indexing [26, 120]; automatic audio-visual speech recognition [106]; biometric audiovisual speech synchrony [20]; multi-sensor management for information fusion [146]; face rec”
Robotic Tactile Perception of Object Properties: A Review (2017, neutral): “tions where it would be impossible using the information from each sensor separately [177].”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “8,195 Additionally, the spatial distribution of these sensors can enable engineers to increase the SHM system ’ s observability.”
A Review of Data Fusion Techniques (2013, background): “Luo et al. [5] provided the following four abstraction levels: (1) signal level: directly addresses the signals that are acquired from the sensors; (2) pixel level: operates at the image level and could be used to improve image processing tasks; (3) characteristic: employs features that are…”
Approaches to Multisensor Data Fusion in Target Tracking: A Survey (2006, background): “Multi Sensor Data Fusion (MSDF) is used in many diverse fields, although most of the literature addresses the fields of military target tracking [4] or autonomous robotics [5].”
Fusion Techniques for Reliable Information: A Survey (2010, background): “In pervasive and ubiquitous computing applications such as smart homes, offices, hospitals, and spaces [36], [42], [59], [61], [88], [107], some fusion techniques have been proposed to reduce uncertainty and achieve reliable data processing and analysis in a fusion system as shown in Figure 1.”
A Survey on Truth Discovery (2015, methodology): “ or clustering on multiple views jointly. Rank aggregation [23,33] has a different input/output space with truth discovery as it focuses on the aggregation of ranking functions. In sensor data fusion [36,41], the source reliability is not considered and usually all the sources are treated indistingu”
Sensor Technologies for Intelligent Transportation Systems (2018, background): “(d) The level of data abstraction: raw measurement, signals and characteristics or decisions [104].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, positive): “[128], three levels of information fusion can be identified: low-level with raw data, mid-level with features and high-level with objects.”