The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system.

This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.

This paper presents a novel algorithm for fusing monocular vision and inertial information for quadrotor Micro Air Vehicles by incorporating the unique dynamic characteristics of that platform into the state estimation process. The dynamics of a quadrotor is unique in that a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. By exploiting these dynamic characteristics, we show that all vehicle states, including the absolute scale, become observable in all motion patterns. This distinguishes our method with other visual-inertial fusion methods, which either assume zero accelerometer bias, or require “sufficiently exciting” motion, such as non-zero acceleration, to ensure observability of the scale. The advantages of our method over existing visual-inertial fusion algorithms are proved through a theoretical analysis using Lie Derivatives and verified by extensive simulations and experiments.