Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

TENet: Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network.

Sensors (Basel, Switzerland)·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception.

Licong Zhuang¹, Xiaorong Zhong¹, Linjie Xu²

¹Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Yutang Street, Guangming District, Shenzhen 518132, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This survey reviews visual Simultaneous Localization And Mapping (SLAM) for autonomous Unmanned Aerial Vehicles (UAVs). It details advancements in real-time performance, texture-less, and dynamic environments for improved UAV navigation.

Keywords:

NeRF UAV feature extraction localization odometry perception visal–inertial SLAM visual SLAM

More Related Videos

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Jun 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Robotics and Artificial Intelligence
Computer Vision
Autonomous Systems

Background:

Localization and perception are fundamental for autonomous Unmanned Aerial Vehicle (UAV) applications.
Simultaneous Localization And Mapping (SLAM) is a key technology for these tasks, evolving with hardware, multi-sensor, and AI advancements.

Purpose of the Study:

To survey the development of visual SLAM and its application in UAVs.
To review state-of-the-art algorithms addressing challenges in visual SLAM for UAVs.
To outline research progression and future directions in UAV localization and perception.

Main Methods:

Review of recent and state-of-the-art visual SLAM algorithms.
Analysis of solutions for real-time performance, texture-less, and dynamic environments.
Discussion of visual-inertial fusion and learning-based enhancements for UAVs.

Main Results:

Identified critical problems and solutions in visual SLAM for UAVs.
Highlighted the role of visual-inertial fusion and learning-based methods.
Provided comprehensive preliminaries including algorithm components, camera configuration, and data processing.

Conclusions:

Visual SLAM is crucial for autonomous UAVs, with ongoing advancements.
Future trends point towards enhanced real-time performance, robustness in challenging environments, and AI integration.
The survey offers a decade-long perspective on visual SLAM for UAVs, identifying research gaps and future opportunities.