Abstract:
An autonomous robot acting in an unknown dynamic environment requires a detailed understanding
of its surroundings. This information is provided by mapping algorithms which are
necessary to build a sensory representation of the environment and the vehicle states. This aids
the robot to avoid collisions with complex obstacles and to localize in six degrees of freedom i.e.
x, y, z, roll, pitch and yaw angle. This process, wherein, a robot builds a sensory representation
of the environment while estimating its own position and orientation in relation to those sensory
landmarks, is known as Simultaneous Localisation and Mapping (SLAM).
A common method for gauging environments are laser scanners, which enable mobile robots to
scan objects in a non-contact way. The use of laser scanners for SLAM has been studied and
successfully implemented. In this project, sensor fusion combining laser scanning and real time
image processing is investigated. Hence, this project deals with the implementation of a Visual
SLAM algorithm followed by design and development of a quadrotor platform which is equipped
with a camera, low range laser scanner and an on-board PC for autonomous navigation and
mapping of unstructured indoor environments.
This report presents a thorough account of the work done within the scope of this project.
It presents a brief summary of related work done in the domain of vision based navigation
and mapping before presenting a real time monocular vision based SLAM algorithm. A C++
implementation of the visual slam algorithm based on the Extended Kalman Filter is described.
This is followed by the design and development of the quadrotor platform. First, the baseline
speci cations are described followed by component selection, dynamics modelling, simulation
and control. The autonomous navigation algorithm is presented along with the simulation
results which show its suitability to real time application in dynamic environments. Finally,
the complete system architecture along with
ight test results are described.
