Visual Navigation for Mobile Robots: A Survey

By Francisco Bonin-Font, Alberto Ortiz, Gabriel Oliver. 2008

643 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Ocean Engineering

Citations in the corpus

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.”
Target-driven visual navigation in indoor scenes using deep reinforcement learning (2016, methodology): “A survey of visual navigation methods can be found in [20].”
Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments (2019, background): “C LASSIC robot navigation is concerned with reaching goals while avoiding collisions [1], [2].”
Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, background): “[33, 63], employs the capability of the visual sensor”
Robot Programming by Demonstration (2010, background): “The decreasing cost of computer vision systems in conjunction with an increase in performance render them more attractive as the primary sensor in mobile robot navigation [5,8,12].”
Bayesian Relational Memory for Semantic Visual Navigation (2019, background): “Traditional approaches (like SLAM) build metric maps via sensory signals, which is subsequently used for planning [17, 4, 51, 16].”
A survey on vision-based UAV navigation (2018, background): “Since laser scanners can easily access 3D point clouds with relatively good quality, they are very popular in ground mobile robots (Bonin-Font, Ortiz, and Oliver 2008).”
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN) (2020, methodology): “Classical map-based methods for visual navigation have been studied for years [Bonin-Font et al., 2008].”
Benchmarking Classic and Learned Navigation in Complex 3D Environments (2019, background): “Navigation research has a long history in robotics [5, 40].”
Synthetic Data for Deep Learning (2019, background): “Possible applications include SLAM, motion planning, and motion for control for self-driving cars (urban navigation) [177, 346, 411, 435], unmanned aerial vehicles [11, 126, 305], and more; see also general surveys of computer vision for mobile robot navigation [68,142] and perception and control fo”
Rearrangement: A Challenge for Embodied AI (2020, background): “Consequently, rearrangement can be used to investigate agent navigation capabilities, a topic that is of interest to both the robotics and vision communities [8].”
Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks (2019, methodology): “We apply SMT on a set of visual navigation tasks, which have a long history in computer vision and robotics [6, 10, 39].”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “Bonin-Font et al. (2008) [10] discussed visual navigation for mobile robotics and divided the techniques into map-based navigation and mapless-based navigation.”
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators (2019, background): “Interactive Navigation: While the literature on autonomous robot navigation is vast and pro-liﬁc [23, 24, 25], less attention has been paid to navigation problems that require interactions with the environment, what we call Interactive Navigation.”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “For a review of applications of different maps in mobile robotics, see (Bonin-Font et al., 2008).”