Monocular vision based indoor simultaneous localisation and mapping for quadrotor platform

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.