A Multi-Sensorial Simultaneous Localization and Mapping SLAM System for Low-Cost Micro Aerial Vehicles in GPS-Denied EnvironmentsReport as inadecuate


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


A Multi-Sensorial Simultaneous Localization and Mapping SLAM System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments - Download this document for free, or read online. Document in PDF available to download.

Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain





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Academic Editor: Gonzalo Pajares Martinsanz

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





Author: Elena López * , Sergio García, Rafael Barea, Luis M. Bergasa, Eduardo J. Molinos, Roberto Arroyo, Eduardo Romera and Samuel Pardo

Source: http://mdpi.com/



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