Integrated Guidance Navigation and Control for a Fully Autonomous Indoor UAS

Abstract

This paper describes the details of a Quadrotor miniature unmanned aerial system capable of autonomously exploring cluttered indoor areas without relying on any external navigational aids such as GPS. A streamlined Simultaneous Localization and Mapping (SLAM) algorithm is implemented onboard the vehicle to fuse information from a scanning laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. This state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. To ensure the SLAM algorithm has sufficient information to form a reliable solution, the guidance algorithm ensures the vehicle approaches frontier waypoints through a path that remains within sensor range of indoor structures. Along with a detailed description of the system, simulation and hardware testing results are presented.

Relative navigation and control of a hexacopter

“…However, a majority of the current research is implemented on ground vehicles without including control. A few implementations on quadrotors using laser scanners have been completed [1], [2] but strong assumptions about the nature of the environment are required. We are primarily interested in implementing vision-based navigation in control feedback for aerial vehicles.…”

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

“…The systems developed by Bachrach et al [5][6][7] employ two groups of measurements, where the IMU readings are treated as measurements of the attitude and accelerations, and laser scan-matching outputs are incorporated as position and heading measurements. In contrast, the state estimation scheme in [8][9][10] utilizes the gyroscope and accelerometer measurements as noisy inputs of the state propagation model, while the attitude error is estimated as part of the MAV states and is used to correct the final altitude estimates. Similarly, EKF-based approaches have also been applied to visual-aided state estimation schemes of MAVs.…”

“…There are several variants and implementations of the EKFs, depending on the formulation of the system dynamics and observation model, as well as the representation of attitude and estimation errors. Typical examples of the conventional EKF-based state estimation scheme proposed by Bachrach et al [5][6][7], Chowdhary et al [8,9] and Sobers et al [10] utilize laser scan-matching algorithms to provide position and heading measurements of the MAV, and fuse these measurements with inertial information via EKFs. The systems developed by Bachrach et al [5][6][7] employ two groups of measurements, where the IMU readings are treated as measurements of the attitude and accelerations, and laser scan-matching outputs are incorporated as position and heading measurements.…”

“…Laser rangefinders have been successfully implemented in previous MAV systems for indoor exploration and mapping [5][6][7][8][9][10]. However, laser rangefinders can only provide distance measurements inside the 2D sensing plane of the sensor, thus their effectiveness is restricted to environments characterized with vertical structures.…”

Indoor Exploration Using a μUAV and a Spherical Geometry Based Visual System

“…This will mean that, if the system is motionless ( = 0), only the gravity will be present, allowing the system to have a vertical reference from the gravity, and calculate the orientation ݊ of the UAV using (12).…”