A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

Experimental and simulation study results for video landmark acquisition and tracking technology

A synopsis of related Earth observation technology is provided and includes surface-feature tracking, generic feature classification and landmark identification, and navigation by multicolor correlation. With the advent of the Space Shuttle era, the NASA role takes on new significance in that one can now conceive of dedicated Earth resources missions. Space Shuttle also provides a unique test bed for evaluating advanced sensor technology like that described in this report. As a result of this type of rationale, the FILE OSTA-1 Shuttle experiment, which grew out of the Video Landmark Acquisition and Tracking (VILAT) activity, was developed and is described in this report along with the relevant tradeoffs. In addition, a synopsis of FILE computer simulation activity is included. This synopsis relates to future required capabilities such as landmark registration, reacquisition, and tracking

Towards markerless orthopaedic navigation with intuitive Optical See-through Head-mounted displays

The potential of image-guided orthopaedic navigation to improve surgical outcomes has been well-recognised during the last two decades. According to the tracked pose of target bone, the anatomical information and preoperative plans are updated and displayed to surgeons, so that they can follow the guidance to reach the goal with higher accuracy, efficiency and reproducibility. Despite their success, current orthopaedic navigation systems have two main limitations: for target tracking, artificial markers have to be drilled into the bone and calibrated manually to the bone, which introduces the risk of additional harm to patients and increases operating complexity; for guidance visualisation, surgeons have to shift their attention from the patient to an external 2D monitor, which is disruptive and can be mentally stressful. Motivated by these limitations, this thesis explores the development of an intuitive, compact and reliable navigation system for orthopaedic surgery. To this end, conventional marker-based tracking is replaced by a novel markerless tracking algorithm, and the 2D display is replaced by a 3D holographic Optical see-through (OST) Head-mounted display (HMD) precisely calibrated to a user's perspective. Our markerless tracking, facilitated by a commercial RGBD camera, is achieved through deep learning-based bone segmentation followed by real-time pose registration. For robust segmentation, a new network is designed and efficiently augmented by a synthetic dataset. Our segmentation network outperforms the state-of-the-art regarding occlusion-robustness, device-agnostic behaviour, and target generalisability. For reliable pose registration, a novel Bounded Iterative Closest Point (BICP) workflow is proposed. The improved markerless tracking can achieve a clinically acceptable error of 0.95 deg and 2.17 mm according to a phantom test. OST displays allow ubiquitous enrichment of perceived real world with contextually blended virtual aids through semi-transparent glasses. They have been recognised as a suitable visual tool for surgical assistance, since they do not hinder the surgeon's natural eyesight and require no attention shift or perspective conversion. The OST calibration is crucial to ensure locational-coherent surgical guidance. Current calibration methods are either human error-prone or hardly applicable to commercial devices. To this end, we propose an offline camera-based calibration method that is highly accurate yet easy to implement in commercial products, and an online alignment-based refinement that is user-centric and robust against user error. The proposed methods are proven to be superior to other similar State-of- the-art (SOTA)s regarding calibration convenience and display accuracy. Motivated by the ambition to develop the world's first markerless OST navigation system, we integrated the developed markerless tracking and calibration scheme into a complete navigation workflow designed for femur drilling tasks during knee replacement surgery. We verify the usability of our designed OST system with an experienced orthopaedic surgeon by a cadaver study. Our test validates the potential of the proposed markerless navigation system for surgical assistance, although further improvement is required for clinical acceptance.Open Acces

Scarring of Florida’s seagrasses: assessment and management options

Management programs that address scarring of seagrasses should be based on an approach that involves (1) education, (2) channel marking, (3) increased enforcement, and (4) limited-motoring zones. Aerial monitoring and photography of the managed area are essential in evaluating the effectiveness of a program. Management programs that use this multifaceted approach have been instituted by a few local governments and at several state parks. Initial results of the programs indicate that in some areas seagrass scarring has been reduced but that in other areas emphasis may need to be increased on one or more of the components of the four-point approach. A statewide management plan is needed to address the most egregious scarring over large areas that may be difficult to regulate at the local-government level

The application of airborne imaging radars (L and X-band) to earth resources problems

For abstract, see N75-24064

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy