Markus W. Achtelik

Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments

The combination of visual and inertial sensors has proved to be very popular in robot navigation and, in particular, Micro Aerial Vehicle (MAV) navigation due the flexibility in weight, power consumption and low cost it offers. At the same time, coping with the big latency between inertial and visual measurements and processing images in real-time impose great research challenges. Most modern MAV navigation systems avoid to explicitly tackle this by employing a ground station for off-board processing. In this paper, we propose a navigation algorithm for MAVs equipped with a single camera and an Inertial Measurement Unit (IMU) which is able to run onboard and in real-time. The main focus here is on the proposed speed-estimation module which converts the camera into a metric body-speed sensor using IMU data within an EKF framework. We show how this module can be used for full self-calibration of the sensor suite in real-time. The module is then used both during initialization and as a fall-back solution at tracking failures of a keyframe-based VSLAM module. The latter is based on an existing high-performance algorithm, extended such that it achieves scalable 6DoF pose estimation at constant complexity. Fast onboard speed control is ensured by sole reliance on the optical flow of at least two features in two consecutive camera frames and the corresponding IMU readings. Our nonlinear observability analysis and our real experiments demonstrate that this approach can be used to control a MAV in speed, while we also show results of operation at 40Hz on an onboard Atom computer 1.6 GHz.

Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments

In this paper, we present our latest achievements towards the goal of autonomous flights of an MAV in unknown environments, only having a monocular camera as exteroceptive sensor. As MAVs are highly agile, it is not sufficient to directly use the visual input for position control at the framerates that can be achieved with small onboard computers. Our contributions in this work are twofold. First, we present a solution to overcome the issue of having a low frequent onboard visual pose update versus the high agility of an MAV. This is solved by filtering visual information with inputs from inertial sensors. Second, as our system is based on monocular vision, we present a solution to estimate the metric visual scale aid of an air pressure sensor. All computation is running onboard and is tightly integrated on the MAV to avoid jitter and latencies. This framework enables stable flights indoors and outdoors even under windy conditions.

Monocular Vision for Long-term Micro Aerial Vehicle State Estimation: A Compendium

The recent technological advances in Micro Aerial Vehicles (MAVs) have triggered great interest in the robotics community, as their deployability in missions of surveillance and reconnaissance has now become a realistic prospect. The state of the art, however, still lacks solutions that can work for a long duration in large, unknown, and GPS-denied environments. Here, we present our visual pipeline and MAV state-estimation framework, which uses feeds from a monocular camera and an Inertial Measurement Unit (IMU) to achieve real-time and onboard autonomous flight in general and realistic scenarios. The challenge lies in dealing with the power and weight restrictions onboard a MAV while providing the robustness necessary in real and long-term missions. This article provides a concise summary of our work on achieving the first onboard vision-based power-on-and-go system for autonomous MAV flights. We discuss our insights on the lessons learned throughout the different stages of this research, from the conception of the idea to the thorough theoretical analysis of the proposed framework and, finally, the real-world implementation and deployment. Looking into the onboard estimation of monocular visual odometry, the sensor fusion strategy, the state estimation and self-calibration of the system, and finally some implementation issues, the reader is guided through the different modules comprising our framework. The validity and power of this framework are illustrated via a comprehensive set of experiments in a large outdoor mission, demonstrating successful operation over flights of more than 360 m trajectory and 70 m altitude change.

Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments

Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.

The EuRoC micro aerial vehicle datasets

This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The datasets contain synchronized stereo images, IMU measurements and accurate ground truth. The first batch of datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data. It was collected in an industrial environment and contains millimeter accurate position ground truth from a laser tracking system. The second batch of datasets is aimed at precise 3D environment reconstruction and was recorded in a room equipped with a motion capture system. The datasets contain 6D pose ground truth and a detailed 3D scan of the environment. Eleven datasets are provided in total, ranging from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination, enabling researchers to thoroughly test and evaluate their algorithms. All datasets contain raw sensor measurements, spatio-temporally aligned sensor data and ground truth, extrinsic and intrinsic calibrations and datasets for custom calibrations.

A robust and modular multi-sensor fusion approach applied to MAV navigation

It has been long known that fusing information from multiple sensors for robot navigation results in increased robustness and accuracy. However, accurate calibration of the sensor ensemble prior to deployment in the field as well as coping with sensor outages, different measurement rates and delays, render multi-sensor fusion a challenge. As a result, most often, systems do not exploit all the sensor information available in exchange for simplicity. For example, on a mission requiring transition of the robot from indoors to outdoors, it is the norm to ignore the Global Positioning System (GPS) signals which become freely available once outdoors and instead, rely only on sensor feeds (e.g., vision and laser) continuously available throughout the mission. Naturally, this comes at the expense of robustness and accuracy in real deployment. This paper presents a generic framework, dubbed MultiSensor-Fusion Extended Kalman Filter (MSF-EKF), able to process delayed, relative and absolute measurements from a theoretically unlimited number of different sensors and sensor types, while allowing self-calibration of the sensor-suite online. The modularity of MSF-EKF allows seamless handling of additional/lost sensor signals during operation while employing a state buffering scheme augmented with Iterated EKF (IEKF) updates to allow for efficient re-linearization of the prediction to get near optimal linearization points for both absolute and relative state updates. We demonstrate our approach in outdoor navigation experiments using a Micro Aerial Vehicle (MAV) equipped with a GPS receiver as well as visual, inertial, and pressure sensors.

Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV

In this paper, we present a versatile framework to enable autonomous flights of a Micro Aerial Vehicle (MAV) which has only slow, noisy, delayed and possibly arbitrarily scaled measurements available. Using such measurements directly for position control would be practically impossible as MAVs exhibit great agility in motion. In addition, these measurements often come from a selection of different onboard sensors, hence accurate calibration is crucial to the robustness of the estimation processes. Here, we address these problems using an EKF formulation which fuses these measurements with inertial sensors. We do not only estimate pose and velocity of the MAV, but also estimate sensor biases, scale of the position measurement and self (inter-sensor) calibration in real-time. Furthermore, we show that it is possible to obtain a yaw estimate from position measurements only. We demonstrate that the proposed framework is capable of running entirely onboard a MAV performing state prediction at the rate of 1 kHz. Our results illustrate that this approach is able to handle measurement delays (up to 500ms), noise (std. deviation up to 20 cm) and slow update rates (as low as 1 Hz) while dynamic maneuvers are still possible. We present a detailed quantitative performance evaluation of the real system under the influence of different disturbance parameters and different sensor setups to highlight the versatility of our approach.

Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance

Recent development showed that Micro Aerial Vehicles (MAVs) are nowadays capable of autonomously take off at one point and land at another using only one single camera as exteroceptive sensor. During the flight and landing phase the MAV and user have, however, little knowledge about the whole terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a textured 3D mesh on sparse 3D point features of the scene. We use the same feature points to localize and control the vehicle in the 3D space as we do for building the 3D terrain reconstruction mesh. This enables us to reconstruct the terrain without significant additional cost and thus in real-time. Experiments show that the MAV is easily guided through an unknown, GPS denied environment. Obstacles are recognized in the iteratively built 3D terrain reconstruction and are thus well avoided.

Visual-inertial SLAM for a small helicopter in large outdoor environments

In this video, we present our latest results towards fully autonomous flights with a small helicopter. Using a monocular camera as the only exteroceptive sensor, we fuse inertial measurements to achieve a self-calibrating power-on-and-go system, able to perform autonomous flights in previously unknown, large, outdoor spaces. Our framework achieves Simultaneous Localization And Mapping (SLAM) with previously unseen robustness in onboard aerial navigation for small platforms with natural restrictions on weight and computational power. We demonstrate successful operation in flights with altitude between 0.2-70 m, trajectories with 350 m length, as well as dynamic maneuvers with track speed of 2 m/s. All flights shown are performed autonomously using vision in the loop, with only high-level waypoints given as directions.

RotorS—A Modular Gazebo MAV Simulator Framework

In this chapter we present a modular Micro Aerial Vehicle (MAV) simulation framework, which enables a quick start to perform research on MAVs. After reading this chapter, the reader will have a ready to use MAV simulator, including control and state estimation. The simulator was designed in a modular way, such that different controllers and state estimators can be used interchangeably, while incorporating new MAVs is reduced to a few steps. The provided controllers can be adapted to a custom vehicle by only changing a parameter file. Different controllers and state estimators can be compared with the provided evaluation framework. The simulation framework is a good starting point to tackle higher level tasks, such as collision avoidance, path planning, and vision based problems, like Simultaneous Localization and Mapping (SLAM), on MAVs. All components were designed to be analogous to its real world counterparts. This allows the usage of the same controllers and state estimators, including their parameters, in the simulation as on the real MAV.