Monocular vision SLAM for indoor aerial vehicles
By Koray Çelik, Arun Kumar Somani. 2009
121 citations recorded by the source corpus.
This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping (SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic 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
- J. Bagnell
- Ashutosh Saxena
- Stéphane Ross
- Tsuhan Chen
- Sergey Levine
- Joyce Chen
- Martial Hebert
- Cooper Bills
In Computer Vision and Pattern Recognition
- Sergey Levine
- Luc Van Gool
- Jitendra Malik
- Andrew Zisserman
- Abhinav Kumar Gupta
- Martial Hebert
- Trevor Darrell
- Bernt Schiele
In Electrical and Electronic Engineering
- Joyce Chen
- Martial Hebert
- Cooper Bills
- Ashutosh Saxena
- J. Bagnell
- Dong Ki Kim
- Tsuhan Chen
- J. Urgen Schmidhuber
Citations in the corpus
- Learning monocular reactive UAV control in cluttered natural environments (2012, background): “ger and more expensive MAVs than what we aim for are required. Accurate depth estimation and localization is also possible with visual sensors using stereo cameras [16] or in a moving monocular setup [17]. A single, cheap camera is enough to create sparse [11] or even dense maps [8] of the environme”
- Autonomous MAV flight in indoor environments using single image perspective cues (2011, methodology): “One can also build a map of the environment using Visual SLAM (e.g., [26], [27], [28]), in which the map is built using images captured from a camera.”
- Deep Neural Network for Real-Time Autonomous Indoor Navigation (2015, methodology): “Celik et al [14] presented autonomous indoor navigation based on SLAM using monocular vision.”
- A survey on vision-based UAV navigation (2018, unknown): “Celik et al. (2009) presented a visual SLAM-based system for indoor environment navigation, where the layout is unknown and without the aids of GPS. UAVs are only equipped with a single camera to estimate the state and range.”