High-frequency MAV state estimation using low-cost inertial and optical flow measurement units

Abstract

Abstract-This paper develops a simple and low-cost method for 3D, high-rate vehicle state estimation, specially designed for free-flying Micro Aerial Vehicles (MAVs). We fuse observations from inertial measurement units and the recently appeared lowcost optical flow smart cameras. These smart cameras integrate a sonar altimeter, a triaxial gyrometer and an optical flow sensor, and directly provide metric ego-motion information in the form of body velocities and altitude. Compared to stateof-the-art visual-inertial odometry methods, we are able to drastically reduce the computational load in the main processor unit, and obtain an accurate estimation of the vehicle state at a high update rate of 100Hz. We thus extend the current use of these smart cameras from hovering purposes to odometry estimation. In order to propose a simple algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside a large number of algorithm variations, using simulations and real experiments with precise ground-truth. We observe that the marginal performance gain attained with these algorithm improvements does not pay for the effort of implementing them. We conclude that a classical EKF in its simplest form is sufficient for providing motion estimates that coherently exploit the available measurements.

A Self-contained Teleoperated Quadrotor: On-Board State-Estimation and Indoor Obstacle Avoidance

“…The estimate resulting from (14) is inherently encumbered with an accumulating error and noise resulting from inaccuracy in the estimation of system's orientationR H k Q k . The estimate's precision and accuracy can be increased using additional measurements, i.e., the optical flow and the distance to the ground [21], [22].…”

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

“…In addition, it delivers horizontal velocities in metric scale, computed from all the input data. In this work, we improve the solution presented in our previous work (Santamaria-Navarro et al, 2015) with the following modifications. We discard the internally computed linear velocities and take advantage of the raw flow to define a better observation function directly in the flow space.…”

“…Notice the model difference with respect to a sonar (Santamaria-Navarro et al, 2015) which directly retrieves the platform height.…”

“…In this paper we provide the following contributions with respect to our previous conference paper (Santamaria-Navarro et al, 2015), in which we originally addressed the VIO problem. First, we define the observation function directly in the flow space instead of using pre-processed planar velocities.…”

Aerial Robotic Manipulation

“…During a aerial manipulation mission, the focus of our work is not at building maps but at localizing the platform, thus we concentrate on VIO. The methods described in this chapter summarize the work presented in [15] and [16].…”