Visual simultaneous localization and mapping: a survey

By Jorge Fuentes-Pacheco, José Ruíz Ascencio, Juan Manuel Rendón-Mancha. 2012

923 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Robotics & Control

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Citations in the corpus

A Point Set Generation Network for 3D Object Reconstruction from a Single Image (2016, background): “3D reconstruction from single images While most researches focus on multi-view geometry such as SFM and SLAM [10, 9], ideally, one expect that 3D can be reconstructed from the abundant single-view images.”
A Survey of Embodied AI: From Simulators to Research Tasks (2021, background): “While classic navigation approaches [82] are usually composed of hand-engineered sub-components like localization, mapping [83], path planning [84, 85] and locomotion, the visual navigation in embodied AI aims to learn these navigation systems from data.”
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction (2016, neutral): “One typical assumption is that features can be matched across views [3,4,5,6] as hypothesized by the majority of the methods based on SFM or SLAM [7,8].”
Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, methodology): “often include a variety of assumptions [5] that may not hold in practice, such as static environments or absence of transparent objects.”
Cognitive Mapping and Planning for Visual Navigation (2017, background): “Mapping has been well studied in computer vision and robotics in the form of structure from motion and simultaneous localization and mapping (Fuentes-Pacheco et al. 2015; Izadi et al. 2011; Henry et al. 2010; Snavely et al. 2008) with a variety of sensing modalities such as range sensors, RGB…”
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, background): “SLAM algorithms aim to estimate the pose of a robot while, at the same time, constructing a representation of the explored zone (visual SLAM algorithms rely on visual sensors) [66].”
Object Goal Navigation using Goal-Oriented Semantic Exploration (2020, methodology): “ilding obstacle maps both in 2D and 3D using structure from motion and Simultaneous Localization and Mapping (SLAM) [20, 22, 41]. We defer the interested readers to the survey byFuentes-Pacheco et al.[13] on SLAM. Some of the more relevant works incorporate semantics in the map using probabilistic g”
Unifying Map and Landmark Based Representations for Visual Navigation (2017, background): “Extending this to the setting of unknown environments, simultaneous localization and mapping (SLAM) algorithms [7,9,10,13,19] aim to both construct a map and localize the robot.”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “For a more detailed review of ORB-SLAM or other standard feature-based techniques, interested readers can follow [40] or [180].”
A Survey on Structure from Motion (2017, methodology): “For alternative methods and further discussion on SLAM, we refer the reader to other sources, e.g., [6, 18, 87], and the references therein.”
Computer vision in automated parking systems: Design, implementation and challenges (2017, methodology): “1, via visual SLAM/bundle adjustment techniques [25], although there are other methods for visual odometry [26].”
FILM: Following Instructions in Language with Modular Methods (2021, methodology): “Simultaneous Localization and Mapping (SLAM) methods, which build 2D or 3D obstacle maps, have been widely used (Fuentes-Pacheco et al., 2015; Izadi et al., 2011; Snavely et al., 2008).”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, background): “Note that there exist survey papers on vSLAM algorithms propposed till 2011 [8, 9].”
TartanAir: A Dataset to Push the Limits of Visual SLAM (2020, background): “SLAM) has become an important component for most autonomous systems [1].”
LDSO: Direct Sparse Odometry with Loop Closure (2018, background): “Simultaneous Localization and Mapping (SLAM) has been an active research area in computer vision and robotics for several decades since the 1980s [2], [3].”
Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images (2019, methodology): “Traditional methods, such as Structure from Motion (SfM) [13] and Si-multaneous Localization and Mapping (SLAM) [5], match image features across views.”