This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Morphometric tools for landmark data: geometry and biology

In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.

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Visual-Inertial Monocular SLAM With Map Reuse

Over the past few years, the application of camera-equipped Unmanned Aerial Vehicles (UAVs) for visually monitoring construction and operation of buildings, bridges, and other types of civil infrastructure systems has exponentially grown. These platforms can frequently survey construction sites, monitor work-in-progress, create documents for safety, and inspect existing structures, particularly for hard-to-reach areas. The purpose of this paper is to provide a concise review of the most recent methods that streamline collection, analysis, visualization, and communication of the visual data captured from these platforms, with and without using Building Information Models (BIM) as a priori information. Specifically, the most relevant works from Civil Engineering, Computer Vision, and Robotics communities are presented and compared in terms of their potential to lead to automatic construction monitoring and civil infrastructure condition assessment.

/pdf/visual-monitoring-of-civil-infrastructure-systems-via-camera-7xo7w3ojtr.pdf

Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works

Vision-based high-speed autonomous landing and cooperative objects grasping - towards the MBZIRC competition

Micro Aerial Vehicles (MAVs) can play an important role in helping to accomplish tasks that are physically too dangerous or complex for humans. A team of aerial robots can cooperatively transport objects that are too large or heavy to be carried by a single MAV. This paper address the design and experimental evaluation of a distributed cooperative control method for cooperative transportation of cable-suspended payloads using MAVs. The key contributions of this paper are: i) a distributed control method for cooperative transportation of cable-suspended payloads with a team of quadrotors is experimentally designed and evaluated and allows to control both the load position and orientation, ii) our solution is light-weight and can run on-board small robots equipped with computationally limited units, iii) we study and analyze the system’s performances and maneuverability based on different cable lengths.

Design and Experimental Evaluation of Distributed Cooperative Transportation of Cable Suspended Payloads with Micro Aerial Vehicles

Autonomous exploration is a longstanding goal of the robotics community. Aerial drone navigation has proven to be especially challenging. The stringent requirements on cost, weight, maneuverability, and power consumption do not allow exploration approaches to easily be employed or adapted to different types of environments. End-to-End Deep Reinforcement Learning (DRL) techniques based on Convolutional Networks approximators, which grant constant-time computation, predefined memory usage, and deliver high visual perception capabilities, represent a very promising alternative to current state of the art solutions relying on metric environment reconstruction. In this work, we address the autonomous exploration problem with aerial robots with a monocular camera based on DRL. Specifically, we propose a novel asymmetric actor-critic model for drone exploration that efficiently leverages ground truth information provided by the simulator environment to speed up learning and enhance final exploration performances. Furthermore, in order to reduce the sim-to-real gap for exploration, we present a novel mixed reality framework that allows an easier, smoother, and safer simulation to real-world transition. Both aspects allow to further exploit the great potential of simulation engines and contribute to reducing the risk associated with directly deploying algorithms on a physical platform with no intermediate step between the simulation and the real world. This is well-known to create several safety concerns and be dangerous when deploying aerial vehicles. Experimental results with a drone exploring multiple environments show the effectiveness of the proposed approach.

Autonomous Single-Image Drone Exploration With Deep Reinforcement Learning and Mixed Reality

Wireless communication at millimeter wave frequencies has attracted considerable attention for the delivery of high-bit-rate connectivity to unmanned aerial vehicles (UAVs). However, conducting the channel measurements necessary to assess communication at these frequencies has been challenging due to the severe payload and power restrictions in commercial UAVs. This work presents a novel lightweight (approximately 1.3 kg) channel measurement system at 28 GHz installed on a commercially available UAV. A ground transmitter equipped with a horn antenna conveys sounding signals to a UAV equipped with a lightweight spectrum analyzer. We demonstrate that the measurements can be highly influenced by the antenna pattern as shaped by the UAV's frame. A calibration procedure is presented to correct for the resulting angular variations in antenna gain. The measurement setup is then validated on real flights from an airstrip at distances in excess of 300 m.

Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz

Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings. In such uses, millimeter wave (mmWave) wireless links can provide high throughput and low latency connectivity between the UAV and a remote command center. However, maintaining reliable aerial communication in the mmWave bands is challenging due to the need to support high speed beam tracking and overcome blockage. This paper provides a simulation study aimed at assessing the feasibility of public safety UAV connectivity through a 5G link at 28 GHz. Real flight motion traces are captured during maneuvers similar to those expected in public safety settings. The motions traces are then incorporated into a detailed mmWave network simulator that models the channel, blockage, beamforming and full 3GPP protocol stack. We show that 5G mmWave communications can deliver throughput up to 1 Gbps with consistent sub ms latency when the base station is located near the mission area, enabling remote offloading of the UAV control and perception algorithms.

Giuseppe Loianno

Papers

Vision-based high-speed autonomous landing and cooperative objects grasping - towards the MBZIRC competition

Design and Experimental Evaluation of Distributed Cooperative Transportation of Cable Suspended Payloads with Micro Aerial Vehicles

Autonomous Single-Image Drone Exploration With Deep Reinforcement Learning and Mixed Reality

Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz

Millimeter Wave Remove UAV Control and Communications for Public Safety Scenarios