Using stereo-based depth cameras outdoors on drones can lead to challenging situations for stereo algorithms calculating a depth map.A false depth value indicating an object close to the drone can confuse obstacle avoidance algorithms and lead to erratic behavior during the drone flight.We analyze the encountered issues from real-world tests together with practical solutions including a post-processing method to modify depth maps against outliers with wrong depth values.

An important criterion for virtual reality experiences is that they are very immersive. The person inside the head-mounted display feels like really being in the virtual environment. While this can be a very pleasant experience, the opposite can happen as well. The concepts of personal spaces and people or unfriendly avatars entering them, can lead to the same discomfort as if it would happen in real life. In this work, we propose to define multi-level artificial barriers for other avatars and objects, respecting the personal spaces as defined by users. We apply this to both interactive rendered environments and as much as possible also to 360 degree photo and video content.

To enable high immersion for virtual reality head-mounted displays (HMDs), a wide field of view of the display is required. Today's consumer solutions are mostly around 90 to 110 degrees field of view. The full human field of view for both eyes together has been measured to be between 200 and 220 degrees. Prototypes of HMDs with such properties have been shown. As the rendering workload increases with more pixels to fill the field of view, we propose a novel rendering method optimized for HMDs that cover the full human field of view. We target lower end HMDs where the cost of eye tracking would increase the price too much. Our method works without eye tracking, making use of certain human vision properties that appear once the full human field of view is covered. We achieve almost twice the rendering performance using our method.

As the applications of Micro Aerial Vehicles (MAVs) get more and more complex, and require highly dynamic motions, it becomes essential to have an accurate dynamic model of the MAV. Such a model can be used for reliable state estimation, control, and for realistic simulation. A good model requires accurate estimates of physical parameters of the system, which we aim to estimate from recorded flight data. In this paper, we present a detailed physical model of the MAV and a maximum likelihood estimation scheme for determining the dominant parameters, such as inertia matrix, center of gravity (CoG) with respect to the IMU, and parameters related to the aerodynamics. To incorporate all information given by the IMU and the physical MAV model, we propose to use two process models in the optimization. We show the effectiveness of the method on simulated data, as well as on a real platform.

In this work, we present a model-based estimation scheme for multi-rotor Micro Aerial Vehicles (MAVs). Although modeling approaches for MAVs have been presented in the past, these models have rarely been used for real-time state estimation onboard MAVs. Building on this work, we identify the most dominant effects and propose an easy-to-use calibration scheme for estimation of the model parameters. Given the calibration estimates for these parameters, we derive a state estimator where the state prediction of the indirect Extended Kalman Filter (EKF) is driven by a MAV model. Solely using measurements from the Inertial Measurement Unit (IMU) and a barometric pressure sensor - both available on almost every MAV - our model-based formulation keeps the estimated velocity of the MAV bounded in all directions, as opposed to state of the art IMU-model driven state estimators onboard MAVs. This is crucial for keeping MAVs airborne safely, for instance in the case of failures or re-initialization of vision based localization systems.

In this work, we present an MAV system that is able to relocalize itself, create consistent maps and plan paths in full 3D in previously unknown environments. This is solely based on vision and IMU measurements with all components running onboard and in real-time. We use visual-inertial odometry to keep the MAV airborne safely locally, as well as for exploration of the environment based on high-level input by an operator. A globally consistent map is constructed in the background, which is then used to correct for drift of the visual odometry algorithm. This map serves as an input to our proposed global planner, which finds dynamic 3D paths to any previously visited place in the map, without the use of teach and repeat algorithms. In contrast to previous work, all components are executed onboard and in real-time without any prior knowledge of the environment.

For autonomous navigation of Micro Aerial Vehicles (MAVs) in cluttered environments, it is essential to detect potential obstacles not only in the direction of flight but in their entire local environment. While there exist systems that do vision based obstacle detection, most of them are limited to a single perception direction. Extending these systems to a multi-directional sensing approach would exhaust the payload limit in terms of weight and computational power. We present a novel light-weight sensor setup comprising of four stereo heads and an inertial measurement unit (IMU) to perform FPGA-based dense reconstruction for obstacle detection in all directions. As the data-rate scales up with the number of cameras we use an FPGA to perform streaming based tasks in real-time and show a light-weight polar-coordinate map to allow a companion computer to fully process the data of all the cameras and perform obstacle detection in real-time. The system is able to process up to 80 frames per second (fps) freely distributed on the four stereo heads while maintaining a low power budget. The perception system including FPGA, image sensors and stereo mounts is 235 g in weight.

With navigation algorithms reaching a certain maturity in the field of mobile robots, the community now focuses on more advanced tasks like path planning towards increased autonomy. While the goal is to efficiently compute a path to a target destination, the uncertainty in the robot's perception cannot be ignored if a realistic path is to be computed. With most state of the art navigation systems providing the uncertainty in motion estimation, here we propose to exploit this information. This leads to a system that can plan safe avoidance of obstacles, and more importantly, it can actively aid navigation by choosing a path that minimizes the uncertainty in the monitored states. Our proposed approach is applicable to systems requiring certain excitations in order to render all their states observable, such as a MAV with visual-inertial based localization. In this work, we propose an approach which takes into account this necessary motion during path planning: by employing Rapidly exploring Random Belief Trees (RRBT), the proposed approach chooses a path to a goal which allows for best estimation of the robot's states, while inherently avoiding motion in unobservable modes. We discuss our findings within the scenario of vision-based aerial navigation as one of the most challenging navigation problem, requiring sufficient excitation to reach full observability.

In this work, we present a powerful, albeit simple position control approach for Micro Aerial Vehicles (MAVs) targeting specifically multicopter systems. Exploiting the differential flatness of four of the six outputs of multicopters, namely position and yaw, we show that the remaining outputs of pitch and roll need not be controlled states, but rather just need to be known. Instead of the common approach of having multiple cascaded control loops (position - velocity - acceleration/attitude - angular rates), the proposed method employs an outer control loop based on dynamic inversion, which directly commands angular rates and thrust. The inner control loop then reduces to a simple proportional controller on the angular rates. As a result, not only does this combination allow for higher bandwidth compared to common control approaches, but also eliminates many mathematical operations (only one trigonometric function is called), speeding up the necessary processing especially on embedded systems. This approach assumes a reliable state estimation framework, which we are able to provide with through previous work. As a result, with this work, we provide the missing elements necessary for a complete approach on autonomous navigation of MAVs.

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.

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.

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.

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.

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.

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.

In this paper, we propose a method to recover the relative pose of two robots in absolute scale and in real-time using one monocular camera on each robot. We achieve this by fusing measurements from the onboard inertial sensors on each platform with information obtained from feature correspondences between the two cameras using an Extended Kalman Filter (EKF). This forms a flexible stereo rig, providing the ability to treat the two robots as one single dynamic sensor, which can adapt to the environment and thus improve environmental mapping, obstacle avoidance and navigation. We demonstrate the power of this approach on both simulation and real datasets, employing two micro aerial vehicles (MAVs) to illustrate successful operation over general 3D motion.

The large collections of datasets for researchers working on the Simultaneous Localization and Mapping problem are mostly collected from sensors such as wheel encoders and laser range finders mounted on ground robots. The recent growing interest in doing visual pose estimation with cameras mounted on micro-aerial vehicles however has made these datasets less useful. In this paper, we describe our work in creating new datasets collected from a sensor suite mounted on a quadrotor platform. Our sensor suite includes a forward looking camera, a downward looking camera, an inertial measurement unit and a Vicon system for groundtruth. We propose the use our datasets as benchmarking tools for future works on visual pose estimation for micro-aerial vehicles. We also show examples of how the datasets could be used for benchmarking visual pose estimation algorithms.