A Systematic Review on Fusion Techniques and Approaches Used in Applications

Abstract

Fusion technologies have rapidly evolved. These technologies are normally customized according to the needs of domains. Despite a large number of publications on intelligence fusion applications for various domains, they are scattered. The aim of this review is to present the state of the art for intelligence fusion applications within a specific domain. We identified three major domains for the purpose, namely robotics, military, and healthcare, during the initial process of the systematic review. These three domains are always in need of superior intelligence. Articles were searched mainly in IEEE Xplore. We limit the range of publications to the year 2014 to 2019, to focus on the most recent publications. We adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to screen, filter and evaluate qualities of each retrieved article. As a result, we retrieved 675 articles at the initial stage of the search, we conducted screening and filtering process and reviewed 153 articles potential articles, and finally, we excluded 36 articles as they do not comply with our quality assessment criteria. Only 117 articles are included. The results of this study are a list of classified applications within the domains and a number of relevant techniques or approaches used in each classified application. The finding of this review showed that the most published works for the use of intelligence fusion are mainly applications in the robotics domain, where mostly used techniques are Kalman Filter and its variants. Outcomes of this study can be a guideline or an insight for researchers to further develop and implement in this field.

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

“…Table 8 a below summarizes the strengths and weaknesses of the sensor fusion approaches: HLF, LLF, and MLF, and presents an overview of the sensor fusion techniques and algorithms for obstacle detection, namely YOLO, SSD, VoxelNet, and PointNet, in Table 8 b. The readers interested in detailed discussions about sensor fusion techniques and algorithms for various applications ranging from perception, including 2D or 3D obstacle detection and lane tracking, to localization and mapping are advised to refer to [ 19 , 20 , 23 , 24 , 25 , 184 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 ].…”

“…Several reviews have been published recently on the topic of multi-sensor fusion, some of them describing the architectural structure and sensor technologies in AVs [ 15 , 20 , 21 ], or focusing on the processing stages like sensor calibration, state estimation, object and tracking [ 22 , 23 , 24 ], or detailing techniques used for multi-sensor fusion, like deep learning-based approaches [ 19 , 25 , 26 ]. Table 1 below summarizes some of the recent studies in sensor and sensor fusion technologies in AD systems.…”

Exploring Factors That Influence the Mainstreaming of Gendered Energy Interventions in Poor Urban Environments: A Structured Literature Review

“…All dates, general topics, and languages were searched for in the Scopus database to ensure full coverage [31]. For Google Scholar and ENERGIA, the search was limited to (i) literature from the years 2015 to 2019, to focus on the most recent works and to prevent the bias that older works may introduce [37]; and (ii) English documents (there is insufficient evidence to conclude that language-restricted meta-analyses lead to biased results [38]). Limitations of the search included the inaccessibility of documents, the language restriction on the Google Scholar search, and the date restrictions on the Google Scholar and ENERGIA databases.…”

“…To eliminate duplicates, a preliminary data refinement on the titles [34] was conducted. Thereafter the titles and abstracts of the remaining 138 documents were screened [34,37,45]. Based on this screening, documents were excluded from the review in the cases of (i) the unavailability of the full-text document; or (ii) the literature type being a thesis, unpublished report, poster, presentation, white literature, or webpage.…”

“…In Phase 3, the eligibility assessments of the 61 full-text documents were conducted using the eligibility criteria ( Figure 2). The whole content of a document was browsed and, based on the eligibility criteria [34,37,45], it was determined whether the document applied and detailed gender mainstreaming in energy interventions, or whether it simply mentioned gender mainstreaming in general. From the 61 documents assessed for eligibility, 27 did not meet the eligibility criteria and were excluded from the qualitative analysis.…”