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

Abstract

The combination of visual and inertial sensors for state estimation has recently found wide echo in the robotics community, especially in the aerial robotics field, due to the lightweight and complementary characteristics of the sensors data. However, most state estimation systems based on visual-inertial sensing suffer from severe processor requirements, which in many cases make them impractical. In this paper, we propose a simple, low-cost and high rate method for state estimation enabling autonomous flight of Micro Aerial Vehicles (MAVs), which presents a low computational burden. The proposed state estimator fuses observations from an inertial measurement unit, an optical flow smart camera and a time-of-flight range sensor. The smart camera provides optical flow measurements up to a rate of 200 Hz, avoiding the computational bottleneck to the main processor produced by all image processing requirements. To the best of our knowledge, this is the first example of extending the use of these smart cameras from hovering-like motions to odometry estimation, producing estimates that are usable during flight times of several minutes. In order to validate and defend the simplest algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside with a large number of algorithm modifications defended in earlier literature on visual-inertial odometry, showing that their impact on filter performance is minimal. To close the control loop, a non-linear controller operating in the special Euclidean group SE(3) is able to drive, based on the estimated vehicle's state, a quadrotor platform in 3D space guaranteeing the asymptotic stability of 3D position and heading. All the estimation and control tasks are solved on board and in real time on a limited computational unit.The proposed approach is validated through simulations and experimental results, which include comparisons with ground-truth data provided by a motion capture system. For the benefit of the community, we make the source code public.

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

“…Hence, the different sensing setups have an effect on what type of filtering is best for each situation. The most commonly used state estimation technique in robotics is the Kalman filter and its variants, such as the Extended Kalman filter (EKF; Gross, Gu, Rhudy, Gururajan, & Napolitano, 2012; Santamaria‐Navarro, Loianno, Solà, Kumar, & Andrade‐Cetto, 2018; Weiss, Achtelik, Chli, & Siegwart, 2012). However, the racing scenario has properties that make it challenging for a Kalman filter.…”

Aerial Robotic Manipulation

“…The algorithm variations that we investigate are shown in Table 1, and are properly defined later in [16]. They are summarized hereafter, together with the key works that defended them.…”

“…where δ q = q{δ θ } exp(δ θ /2) is the orientation error in SO(3) expressed as a unit quaternion -see [16] for details.…”

“…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].…”

Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

“…A non-linear optimisation based approach has also been employed [5], in order to integrate information from an Inertial Measurement Unit (IMU) with seen features detected from stereo camera images, using the BRISK approach [16]. Additionally, a low-cost platform for visual odometry in UAVs has also been recently proposed, using an IMU and a single camera [17], but they focus on precise movement estimation, instead of mapping.…”