Autonomous flight and remote site landing guidance research for helicopters

Automated low-altitude flight and landing in remote areas within a civilian environment are investigated, where initial cost, ongoing maintenance costs, and system productivity are important considerations. An approach has been taken which has: (1) utilized those technologies developed for military applications which are directly transferable to a civilian mission; (2) exploited and developed technology areas where new methods or concepts are required; and (3) undertaken research with the potential to lead to innovative methods or concepts required to achieve a manual and fully automatic remote area low-altitude and landing capability. The project has resulted in a definition of system operational concept that includes a sensor subsystem, a sensor fusion/feature extraction capability, and a guidance and control law concept. These subsystem concepts have been developed to sufficient depth to enable further exploration within the NASA simulation environment, and to support programs leading to the flight test

Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

Detection and estimation of moving obstacles for a UAV

In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

The automation of low-altitude rotorcraft flight depends on the ability to detect, locate, and navigate around obstacles lying in the rotorcraft's intended flightpath. Computer vision techniques provide a passive method of obstacle detection and range estimation, for obstacle avoidance. Several algorithms based on computer vision methods have been developed for this purpose using laboratory data; however, further development and validation of candidate algorithms require data collected from rotorcraft flight. A data base containing low-altitude imagery augmented with the rotorcraft and sensor parameters required for passive range estimation is not readily available. Here, the emphasis is on the methodology used to develop such a data base from flight-test data consisting of imagery, rotorcraft and sensor parameters, and ground-truth range measurements. As part of the data preparation, a technique for obtaining the sensor calibration parameters is described. The data base will enable the further development of algorithms for computer vision-based obstacle detection and passive range estimation, as well as provide a benchmark for verification of range estimates against ground-truth measurements

A parallel implementation of a multisensor feature-based range-estimation method

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer

Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision

In order to improve usability and safety, modern unmanned aerial vehicles (UAVs) are equipped with sensors to monitor the environment, such as laser-scanners and cameras. One important aspect in this monitoring process is to detect obstacles in the flight path in order to avoid collisions. Since a large number of consumer UAVs suffer from tight weight and power constraints, our work focuses on obstacle avoidance based on a lightweight stereo camera setup. We use disparity maps, which are computed from the camera images, to locate obstacles and to automatically steer the UAV around them. For disparity map computation we optimize the well-known semi-global matching (SGM) approach for the deployment on an embedded FPGA. The disparity maps are then converted into simpler representations, the so called U-/V-Maps, which are used for obstacle detection. Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV. One of the fundamental goals of our work was the reduction of development costs by closing the gap between application development and hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for porting our algorithms, which are written in C/C++, to the embedded FPGA. We evaluated our implementation of the disparity estimation on the KITTI Stereo 2015 benchmark. The integrity of the overall realtime reactive obstacle avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in conjunction with two flight simulators.Comment: Accepted in the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Scienc

Tightly Coupled GNSS and Vision Navigation for Unmanned Air Vehicle Applications

This paper explores the unique benefits that can be obtained from a tight integration of a GNSS sensor and a forward-looking vision sensor. The motivation of this research is the belief that both GNSS and vision will be integral features of future UAV avionics architectures, GNSS for basic aircraft navigation and vision for obstacle-aircraft collision avoidance. The paper will show that utilising basic single-antenna GNSS measurements and observables, along with aircraft information derived from optical flow techniques creates unique synergies. Results of the accuracy of attitude estimates will be presented, based a comprehensive Matlab® Simulink® model which re-creates an optical flow stream based on the flight of an aircraft. This paper establishes the viability of this novel integrated GNSS/Vision approach for use as the complete UAV sensor package, or as a backup sensor for an inertial navigation system