Autonomous mobile robots are required to move throughout map the environment, locate themselves, and plan paths between positions. Vision stands out among the other senses for its richness and practicality. Even though there are well-established autonomous navigation solutions, as far as we can tell, no complete autonomous navigation system that is solely based on vision and that is suitable for dynamic indoor environments has fully succeeded. This article presents a systematic literature review of methods and techniques used to solve the complete autonomous navigation problem or its parts: localization, mapping, path planning, and locomotion. The focus of this review lays on vision-based methods for indoor environments and ground robots. A total of 121 studies were considered, comprising methods, conceptual models, and other literature reviews published between 2000 and 2017. To the best of our knowledge, this is the first systematic review about vision-based autonomous navigation suitable for dynamic indoor environments. It addresses navigation methods, autonomous navigation requirements, vision benefits, methods testing, and implementations validation. The results of this review show a deficiency in testing and validation of presented methods, poor requirements specification, and a lack of complete navigation systems in the literature. These results should encourage new works on computer vision techniques, requirements specification, development, integration, and systematic testing and validation of general navigation systems. In addition to these findings, we also present the complete methodology used for the systematic review, which provides a documentation of the process (allowing quality assessment and repeatability), the criteria for selecting and evaluating the studies, and a framework that can be used for future reviews in this research area.

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Aircraft visual inspections, or General Visual Inspections (GVIs), aim at finding damages or anomalies on the exterior and interior surfaces of the aircraft, which might compromise its operation, structure, or safety when flying. Visual inspection is part of the activities of aircraft Maintenance, Repair and Overhaul (MRO). Specialists perform quality inspections to identify problems and determine the type and importance that they will report. This process is time-consuming, subjective, and varies according to each individual. The time that an aircraft stays grounded without flight clearance means financial losses. The main goal of this work is to advance the state-of-the-art of defect detection on aircraft exterior with deep learning and computer vision. We investigate improvements to the accuracy of dent detection. Besides, we investigate new classes of identified defects, such as scratches. We also plan to demonstrate that it is possible to develop a complete system to automate the visual inspection of aircraft exterior using images of the aircraft acquired by drones. We will use deep neural networks for the detection and segmentation of defective regions. This system will aid in the elimination of subjectivity caused by human errors and shorten the time required to inspect an aircraft, bringing benefits to its safety, maintenance, and operation.