FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s). While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex extrinsic dynamics are generated organically through the natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be found at https://flightgoggles.mit.edu. Revision includes description of new FlightGoggles features, such as a photogrammetric model of the MIT Stata Center, new rendering settings, and a Python AP

Design and Development of an Inspection Robotic System for Indoor Applications

The inspection and monitoring of industrial sites, structures, and infrastructure are important issues for their sustainability and further maintenance. Although these tasks are repetitive and time consuming, and some of these environments may be characterized by dust, humidity, or absence of natural light, classical approach relies on large human activities. Automatic or robotic solutions can be considered useful tools for inspection because they can be effective in exploring dangerous or inaccessible sites, at relatively low-cost and reducing the time required for the relief. The development of a paradigmatic system called Inspection Robotic System (IRS) is the main objective of this paper to demonstrate the feasibility of mechatronic solutions for inspection of industrial sites. The development of such systems will be exploited in the form of a tool kit to be flexible and installed on a mobile system, in order to be used for inspection and monitoring, possibly introducing high efficiency, quality and repetitiveness in the related sector. The interoperability of sensors with wireless communication may form a smart sensors tool kit and a smart sensor network with powerful functions to be effectively used for inspection purposes. Moreover, it may constitute a solution for a broad range of scenarios spacing from industrial sites, brownfields, historical sites or sites dangerous or difficult to access by operators. First experimental tests are reported to show the engineering feasibility of the system and interoperability of the mobile hybrid robot equipped with sensors that allow real-time multiple acquisition and storage

Exploitation of time-of-flight (ToF) cameras

This technical report reviews the state-of-the art in the field of ToF cameras, their advantages, their limitations, and their present-day applications sometimes in combination with other sensors. Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modeling and recognition.Preprin

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed

ToF cameras for active vision in robotics

ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited. After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/ foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.This work was supported by the EU project GARNICS FP7-247947, by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510, and by the Catalan Research Commission through SGR-00155Peer Reviewe

INSPIRE Newsletter Spring 2021

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