Autonomous Mapping of Unknown Environments Using a UAV

Examensarbete för masterexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
Master_Thesis_Heikkila_Persson_.pdf8.74 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Title: Autonomous Mapping of Unknown Environments Using a UAV
Authors: Persson, Erik
Heikkilä, Filip
Abstract: Automatic object search in a bounded area can be accomplished using cameracarrying autonomous aerial robots. The system requires several functionalities to solve the task in a safe and efficient way, including finding a navigation and exploration strategy, creating a representation of the surrounding environment and detecting objects visually. Here we create a modular framework and provide solutions to the different subproblems in a simulated environment. The navigation and exploration subproblems are tackled using deep reinforcement learning (DRL). Object and obstacle detection is approached using methods based on the scale-invariant feature transform and the pinhole camera model. Information gathered by the system is used to build a 3D voxel map. We further show that the object detection system is capable of detecting certain target objects with high recall. The DRL approach is able to achieve navigation that avoids collisions to a high degree, but the performance of the exploration policy is suboptimal. Due to the modular character of the solution further improvements of each subsystems can easily be developed independently.
Keywords: Deep reinforcement learning, autonomous exploration and navigation, feature extraction, object detection, voxel map, UAV, modular framework.
Issue Date: 2020
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.