Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.     

Al‐Robotics team: A cooperative multi‐unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al‐Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al‐Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.     

The ETH‐MAV Team in the MBZ International Robotics Challenge

This study describes the hardware and software systems of the Micro Aerial Vehicle (MAV) platforms used by the ETH Zurich team in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The aim was to develop robust outdoor platforms with the autonomous capabilities required for the competition, by applying and integrating knowledge from various fields, including computer vision, sensor fusion, optimal control, and probabilistic robotics. This paper presents the major components and structures of the system architectures and reports on experimental findings for the MAV‐based challenges in the competition. Main highlights include securing the second place both in the individual search, pick, and place the task of Challenge 3 and the Grand Challenge, with autonomous landing executed in less than 1 min and a visual servoing success rate of over for object pickups.     

A general‐purpose task execution framework for manipulation mission of the 2017 Mohamed Bin Zayed International Robotics Challenge

This paper presents the hardware and software of our team's EurecarBot for Challenge 2 in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). Fully automating our robots actions in a real environment required many component technologies for manipulation and vision processing. To perform the complex robotic missions, we developed a task execution framework, which provides a high‐level interface to specify the given tasks. In this study, we focus on the valve operation problem, which was the hardest part of the competition. We also discuss how we overcame the various problems caused by differences between the experimental and the actual competition environments. EurecarBot completed the valve operation mission perfectly in the MBZIRC Grand Challenge and ranked fourth in Challenge 2 and fifth in the Grand Challenge.     

Autonomous landing on a moving vehicle with an unmanned aerial vehicle

This paper addresses the perception, control, and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/hr. The hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year‐long development and preparations for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the system was designed. We propose a novel control system in which a model predictive controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams.     

Team NimbRo at MBZIRC 2017: Autonomous valve stem turning using a wrench

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. In this study, we describe our winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is capable of autonomously solving a valve manipulation task using a wrench tool detected, grasped, and finally used to turn a valve stem. Mario’s omnidirectional base allows both fast locomotion and precise close approach to the manipulation panel. We describe an efficient detector for medium‐sized objects in three‐dimensional laser scans and apply it to detect the manipulation panel. An object detection architecture based on deep neural networks is used to find and select the correct tool from grayscale images. Parametrized motion primitives are adapted online to percepts of the tool and valve stem to turn the stem. We report in detail on our winning performance at the challenge and discuss lessons learned.     

Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

The herein studied problem is motivated by practical needs of our participation in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 in which a team of unmanned aerial vehicles (UAVs) is requested to collect objects in the given area as quickly as possible and score according to the rewards associated with the objects. The mission time is limited, and the most time‐consuming operation is the collection of the objects themselves. Therefore, we address the problem to quickly identify the most valuable objects as surveillance planning with curvature‐constrained trajectories. The problem is formulated as a multivehicle variant of the Dubins traveling salesman problem with neighborhoods (DTSPN). Based on the evaluation of existing approaches to the DTSPN, we propose to use unsupervised learning to find satisfiable solutions with low computational requirements. Moreover, the flexibility of unsupervised learning allows considering trajectory parametrization that better fits the motion constraints of the utilized hexacopters that are not limited by the minimal turning radius as the Dubins vehicle. We propose to use Bézier curves to exploit the maximal vehicle velocity and acceleration limits. Besides, we further generalize the proposed approach to 3D surveillance planning. We report on evaluation results of the developed algorithms and experimental verification of the planned trajectories using the real UAVs utilized in our participation in MBZIRC 2017.     

A mission management system for complex aerial logistics by multiple unmanned aerial vehicles in MBZIRC 2017

In this study, we present a system that manages multiple unmanned aerial vehicles (UAVs) for a search, pickup, and drop mission in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). Three UAVs picked up and dropped 23 circular and rectangular targets into a designated drop box. To control the operation of three UAVs flying over an arena of 90 × 60 m, we designed and integrated a set of technologies into our system: airspace allocation, communication framework among UAVs, anticollision based on geofencing, and a token‐based prioritization for coordination. The proposed UAV system uses a single GPS and its error of a few meters is solved by means of the following component technologies: (a) flight path generator based on one reference point, (b) vision‐based redefinition of a reference point for GPS correction, and (c) calibration of flight path to update the reference point. The pickup‐and‐drop mission is conducted via color‐ and shape‐based vision processing and a magnetic gripper to pickup and drop‐off the targets. Our proposed system is able to successfully manage three UAVs, recognize targets on the ground, and drop the targets into a drop box in the drop zone. Finally, we achieved fourth place among 18 teams in Challenge 3.     

Cooperative autonomous search,grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

This paper addresses the problem of autonomous cooperative localization, grasping and delivering of colored ferrous objects by a team of unmanned aerial vehicles (UAVs). In the proposed scenario, a team of UAVs is required to maximize the reward by collecting colored objects and delivering them to a predefined location. This task consists of several subtasks such as cooperative coverage path planning, object detection and state estimation, UAV self‐localization, precise motion control, trajectory tracking, aerial grasping and dropping, and decentralized team coordination. The failure recovery and synchronization job manager is used to integrate all the presented subtasks together and also to decrease the vulnerability to individual subtask failures in real‐world conditions. The whole system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where it achieved the highest score and won Challenge No. 3—Treasure Hunt. This paper does not only contain results from the MBZIRC 2017 competition but it also evaluates the system performance in simulations and field tests that were conducted throughout the year‐long development and preparations for the competition.     

RANGE–Robust autonomous navigation in GPS‐denied environments

This paper addresses the problem of autonomous navigation of a micro air vehicle (MAV) in GPS‐denied environments. We present experimental validation and analysis for our system that enables a quadrotor helicopter, equipped with a laser range finder sensor, to autonomously explore and map unstructured and unknown environments. The key challenge for enabling GPS‐denied flight of a MAV is that the system must be able to estimate its position and velocity by sensing unknown environmental structure with sufficient accuracy and low enough latency to stably control the vehicle. Our solution overcomes this challenge in the face of MAV payload limitations imposed on sensing, computational, and communication resources. We first analyze the requirements to achieve fully autonomous quadrotor helicopter flight in GPS‐denied areas, highlighting the differences between ground and air robots that make it difficult to use algorithms developed for ground robots. We report on experiments that validate our solutions to key challenges, namely a multilevel sensing and control hierarchy that incorporates a high‐speed laser scan‐matching algorithm, data fusion filter, high‐level simultaneous localization and mapping, and a goal‐directed exploration module. These experiments illustrate the quadrotor helicopter's ability to accurately and autonomously navigate in a number of large‐scale unknown environments, both indoors and in the urban canyon. The system was further validated in the field by our winning entry in the 2009 International Aerial Robotics Competition, which required the quadrotor to autonomously enter a hazardous unknown environment through a window, explore the indoor structure without GPS, and search for a visual target. © 2011 Wiley Periodicals, Inc.     

Air‐ground Matching: Appearance‐based GPS‐denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera‐equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image‐based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air‐ground image‐matching algorithm to search the airborne image of the MAV within a ground‐level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back‐projecting the corresponding image points onto a cadastral three‐dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground‐level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision‐based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over‐season variations, thus outperforming conventional visual place‐recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.     

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem.     

Inertially Aided Visual Odometry for Miniature Air Vehicles in GPS-denied Environments

Unmanned miniature air vehicles (MAVs) have recently become a focus of much research, due to their potential utility in a number of information gathering applications. MAVs currently carry inertial sensor packages that allow them to perform basic flight maneuvers reliably in a completely autonomous manner. However, MAV navigation requires knowledge of location that is currently available only through GPS sensors, which depend on an external infrastructure and are thus prone to reliability issues. Vision-based methods such as Visual Odometry (VO) have been developed that are capable of estimating MAV pose purely from vision, and thus have the potential to provide an autonomous alternative to GPS for MAV navigation. Because VO estimates pose by combining relative pose estimates, constraining relative pose error is the key element of any Visual Odometry system. In this paper, we present a system that fuses measurements from an MAV inertial navigation system (INS) with a novel VO framework based on direct image registration. We use the inertial sensors in the measurement step of the Extended Kalman Filter to determine the direction of gravity, and hence provide error-bounded measurements of certain portions of the aircraft pose. Because of the relative nature of VO measurements, we use VO in the EKF prediction step. To allow VO to be used as a prediction, we develop a novel linear approximation to the direct image registration procedure that allows us to propagate the covariance matrix at each time step. We present offline results obtained from our pose estimation system using actual MAV flight data. We show that fusion of VO and INS measurements greatly improves the accuracy of pose estimation and reduces the drift compared to unaided VO during medium-length (tens of seconds) periods of GPS dropout.     

Model based vehicle detection and tracking for autonomous urban driving

Situational awareness is crucial for autonomous driving in urban environments. This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. Furthermore, we show how to build consistent and efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the Urban Grand Challenge as well as other urban settings.     

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. ¹      

An open-source navigation system for micro aerial vehicles

This paper presents an open-source indoor navigation system for quadrotor micro aerial vehicles (MAVs), implemented in the ROS framework. The system requires a minimal set of sensors including a planar laser range-finder and an inertial measurement unit. We address the issues of autonomous control, state estimation, path-planning, and teleoperation, and provide interfaces that allow the system to seamlessly integrate with existing ROS navigation tools for 2D SLAM and 3D mapping. All components run in real time onboard the MAV, with state estimation and control operating at 1 kHz. A major focus in our work is modularity and abstraction, allowing the system to be both flexible and hardware-independent. All the software and hardware components which we have developed, as well as documentation and test data, are available online.     

基于独立分子量分析的图象分离技术及应用

简要介绍了有关独立分量分析的基本理论和算法,探讨了独立分量分析在序列图象处理方面的应用,提出了基于独立分量分析的运动目标检测新方法,同时用独立分量分析方法对含有运动目标的序列图象进行了独立分量分离的试验,试验中,首先获取序列图象的独立分量和模型混合矩阵,然后将含有背景干扰的独立分量置零,并用混合矩阵进行逆运算,从而获得非常清晰的运动目标轨迹,试验结果表明,这种独立分量分析方法具有良好的盲源分离性能,而且在运动目标检测等方面,基于独立分量分析的检测方法较传统的检测方法更有效。     

Vision-guided flight stability and control for micro air vehicles

Substantial progress has been made recently towards designing, building and test-flying remotely piloted micro air vehicles (MAVs) and small unmanned air vehicles. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a visionguided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for selfstabilized flight and report results on vision-based autonomous flights of duration exceeding 10 min. We conclude with an overview of our on-going and future MAV-related research.     

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU .     

A variant of cyclic pursuit for target tracking applications: theory and implementation

This paper presents a variant of the cyclic pursuit strategy that can be used for target tracking applications. Cyclic pursuit has been extensively used in multi-agent systems for a variety of applications. In order to monitor a target point or to track a slowly moving vehicle, we propose to use a group of non-holonomic vehicles. At equilibrium, the vehicles form a rigid polygonal around the target while encircling it. Necessary conditions for the existence of equilibrium and the stability of equilibrium formations are analysed considering unicycle model of the vehicles. The strategy is then applied to miniature aerial vehicles (MAV) represented by 6-DOF dynamical model. Finally the results are verified in a hardware in-loop simulator in real time, which included all on-board electronics of the MAVs.