Fusion of Unmanned Aerial Vehicle Range and Vision Sensors Using Fuzzy Logic and Particles

Abstract

This paper presents a novel method for fusing data from a UAV's range and vision sensors. The range sensor is used to build an elevation map of the flying area. Fuzzy logic is used to detect red barrels in camera images. The world location of a target on the ground is found by fusing the terrain map with image data using both an extended Kalman filter and a particle filter. The target detection system has been tested using images collected onboard a UAV. The terrain mapping system and the fusion system were both tested in simulation.

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

“…These studies can be grouped depending on their application areas and 3D modeling techniques. Some of the applications of 3D modeling presented in the literature include 3D building modeling [28], 3D city modeling [29], automatic registration for georeferencing [30], railroad center line reconstruction [31], automatic building extraction [32], scene parsing [33], elevation mapping [34], species recognition, height and crown width estimation [35], target localization and relative navigation [36], and visual localization [37]. Modeling techniques used in these studies involve automatic aerial triangulation, coarseto-fine methods [28], digital surface nodes application [29], the iterative closest-point (ICP) algorithm [30], the random sample consensus algorithm [31], the binary space partitioning (BSP) tree [32], the Markov-random-field-based temporal method [33], fuzzy logic and particles [34], multi-scale template matching (MSTM) [35], dynamic bias estimation [36], and localization-by-recognition and vocabulary-tree-based recognition methods [37].…”