Next Article in Journal
A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light
Next Article in Special Issue
Improved Object Detection Using a Robotic Sensing Antenna with Vibration Damping Control
Previous Article in Journal
In Vitro Studies on a Microfluidic Sensor with Embedded Obstacles Using New Antibacterial Synthetic Compounds (1-TDPPO) Mixed Prop-2-en-1-one with Difluoro Phenyl
Previous Article in Special Issue
Optimization of an Optical Test Bench for Tire Properties Measurement and Tread Defects Characterization
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(4), 802; doi:10.3390/s17040802

A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 25 January 2017 / Revised: 27 March 2017 / Accepted: 5 April 2017 / Published: 8 April 2017
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
View Full-Text   |   Download PDF [7111 KB, uploaded 8 April 2017]   |  

Abstract

One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calculates the pose and environment map of different low-cost commercial aerial platforms, whose onboard computing capacity is usually limited. The proposed system adapts to the sensory configuration of the aerial robot, by integrating different state-of-the art SLAM methods based on vision, laser and/or inertial measurements using an Extended Kalman Filter (EKF). To do this, a minimum onboard sensory configuration is supposed, consisting of a monocular camera, an Inertial Measurement Unit (IMU) and an altimeter. It allows to improve the results of well-known monocular visual SLAM methods (LSD-SLAM and ORB-SLAM are tested and compared in this work) by solving scale ambiguity and providing additional information to the EKF. When payload and computational capabilities permit, a 2D laser sensor can be easily incorporated to the SLAM system, obtaining a local 2.5D map and a footprint estimation of the robot position that improves the 6D pose estimation through the EKF. We present some experimental results with two different commercial platforms, and validate the system by applying it to their position control. View Full-Text
Keywords: aerial robots; SLAM; sensor fusion aerial robots; SLAM; sensor fusion
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

López, E.; García, S.; Barea, R.; Bergasa, L.M.; Molinos, E.J.; Arroyo, R.; Romera, E.; Pardo, S. A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments. Sensors 2017, 17, 802.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top