IIIT Hyderabad Publications
Frameworks for Autonomous Navigation of Quadcopter in Indoor and Outdoor Environment
Author: Vishakh Duggal
Report no: IIIT/TH/2016/30
This thesis discusses sophisticated navigation frameworks for autonomous navigation of quadcopter in the indoor and outdoor environment using the frontal monocular camera as its primary exteroceptive sensor. Indoor and outdoor environment vary in their views, composition and constraints thus requiring different sophisticated approaches for navigation. Therefore, individual autonomous navigation frameworks for the indoor and outdoor environment are developed and presented in the thesis. The navigation frameworks for indoor and outdoor environment consists of three major components – perception, planning and control. Initially, navigation framework for autonomous navigation of quadcopter in GPS denied unknown indoor environment is presented. The perception module of navigation framework estimates dense depth map of the environment in real time from video stream obtained from the frontal monocular camera of the quadcopter using our novel supervised Hierarchical Structured Learning (HSL) approach. The proposed HSL approach discretizes the overall depth range into multiple sets. It structures these sets hierarchically and recursively through partitioning the set of classes into two subsets with subsets representing apportioned depth range of the parent set, forming a binary tree. The binary classification method is applied to each internal node of binary tree separately using Support Vector Machine (SVM). Moreover, the depth estimation of each pixel of the image starts from the root node in the top-down approach, classifying repetitively till it reaches any of the leaf node representing its estimated depth. The generated depth map is provided as an input to planning module based on Convolutional Neural Network (CNN), which generates flight planning commands. Finally, control module employs a convex programming technique to generate collision-free minimum time trajectory which follows these flight planning commands and produces appropriate control inputs for the quadcopter. Thereafter, outdoor autonomous navigation framework of quadcopter along with its application in precision agriculture is presented. The proposed system is able to perform plantation monitoring and yield estimation using supervised learning approach while autonomously navigating through the interrow path of pomegranate plantation. A new ‘pomegranate dataset’ comprising of plantation surveillance video and annotated frames capturing the varied stages of pomegranate growth along with the navigation framework are being delivered as a part of this thesis. The capabilities of the proposed system could be described as a twofold framework:(1) plantation monitoring and yield estimation: life cycle stage grad- ing and yield estimation of pomegranate while autonomously navigating through inter-row paths of the plantation using a monocular camera as its primary sensor. (2) Autonomous outdoor navigation framework: a novel navigation framework, which uses GPS way-points and vanishing point based planner and employs convex optimization for generating minimum time trajectory and control. The framework is implemented over Robot Operating System (ROS) middle-ware and ” first to release in Open Source”. A majority of the work in this thesis is attributed to developing an autonomous navigation framework for generic quadcopter which incorporates various theoretical concepts into practical implementation with Robot Operating System ( ROS ) as underlying middle-ware. Repeatable flights of successful nature, confirm the efficacy of the proposed navigation frameworks. The thesis is concluded by demonstrating the applicability of developed navigation framework on a low-cost commercial quadcopter P arrot T M Bebop.
Full thesis: pdf
Centre for Robotics
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