RT info:eu-repo/semantics/article
T1 A multi-sensorial Simultaneous Localization and Mapping (SLAM) system for low-cost micro aerial vehicles in GPS-denied environments
A1 López Guillén, María Elena
A1 García Gonzalo, Sergio
A1 Barea Navarro, Rafael
A1 Bergasa Pascual, Luis Miguel
A1 Molinos Vicente, Eduardo José
A1 Arroyo Contera, Roberto
A1 Romera Carmena, Eduardo
A1 Pardo Alia, Samuel
K1 Sensor fusion
K1 SLAM
K1 Aerial robots
K1 Electrónica
K1 Electronics
AB 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.
PB MDPI
SN 1424-8220
YR 2017
FD 2017-04-08
LK http://hdl.handle.net/10017/43211
UL http://hdl.handle.net/10017/43211
LA eng
NO Comunidad de Madrid
DS MINDS@UW
RD 01-may-2024