Multiple RGB-D sensor-based 3-D reconstruction and localization of indoor environment for mini MAV. (August 2018)

• Record Type: Journal Article
• Authors: Li, Yamin; Wang, Yong; Wang, Dianhong
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 509
• Page End: 524
• Publication Date: 2018-08
• Subjects: Mini MAV -- Visual sensor network -- 3-D reconstruction -- Indoor localization -- Kalman-consensus filter -- Data fusion; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A 3-D VSN composed of only RGB-D sensors is established to enable indoor applications of mini MAVs. A 3-D panoramic reconstruction method of indoor environment using only RGB-D data is present. A KCF-based multi-sensor data fusion estimation approach is proposed to estimate the 3-D global optimal localization and the trajectory of the MAV. Abstract: Micro aerial vehicle (MAV) with lower cost, smaller size and more flexible flight performance plays a greater role in the global position system-denied (GPS-denied) indoor tasks. This paper focuses on the realization of three-dimensional (3-D) panoramic environment reconstruction and target localization without relying on any external navigation aid. By establishing a visual sensor network (VSN) composed of multiple RGB-Depth (RGB-D) sensors, a fast 3-D model of the observed environment is built utilizing an improved speeded up robust feature (SURF) extraction algorithm and iterative closest point-based (ICP-based) reconstruction algorithm. A distributed data fusion algorithm known as kalman-consensus filter (KCF) is then used to estimate a more accurate global position and trajectory of mini MAV. Both software simulations and real indoor flight experiment results demonstrate that the proposed approach achieves a fast and reliable 3-D map of the indoor environment and the accuracy of localization is largely improved. Graphical abstract: