6,144 research outputs found
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing
Belief State Planning for Autonomously Navigating Urban Intersections
Urban intersections represent a complex environment for autonomous vehicles
with many sources of uncertainty. The vehicle must plan in a stochastic
environment with potentially rapid changes in driver behavior. Providing an
efficient strategy to navigate through urban intersections is a difficult task.
This paper frames the problem of navigating unsignalized intersections as a
partially observable Markov decision process (POMDP) and solves it using a
Monte Carlo sampling method. Empirical results in simulation show that the
resulting policy outperforms a threshold-based heuristic strategy on several
relevant metrics that measure both safety and efficiency.Comment: 6 pages, 6 figures, accepted to IV201
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
Timing of autonomous driving software: problem analysis and prospects for future solutions
The software used to implement advanced functionalities in critical domains (e.g. autonomous operation) impairs software timing. This is not only due to the complexity of the underlying high-performance hardware deployed to provide the required levels of computing performance, but also due to the complexity, non-deterministic nature, and huge input space of the artificial intelligence (AI) algorithms used. In this paper, we focus on Apollo, an industrial-quality Autonomous Driving (AD) software framework: we statistically characterize its observed execution time variability and reason on the sources behind it. We discuss the main challenges and limitations in finding a satisfactory software timing analysis solution for Apollo and also show the main traits for the acceptability of statistical timing analysis techniques as a feasible path. While providing a consolidated solution for the software timing analysis of Apollo is a huge effort far beyond the scope of a single research paper, our work aims to set the basis for future and more elaborated techniques for the timing analysis of AD software.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P, the SuPerCom European Research Council (ERC) project under the European Union’s Horizon 2020 research and innovation programme (grant agreement No.772773), and the HiPEAC Network of Excellence. MINECO partially supported Enrico Mezzetti under Juan de la Cierva-Incorporación postdoctoral fellowship (IJCI-2016-27396), and Leonidas Kosmidis under Juan de la Cierva-Formación postdoctoral fellowship (FJCI-2017-34095).Peer ReviewedPostprint (author's final draft
DeepCrashTest: Translating Dashcam Videos to Virtual Tests forAutomated Driving Systems
abstract: The autonomous vehicle technology has come a long way, but currently, there are no companies that are able to offer fully autonomous ride in any conditions, on any road without any human supervision. These systems should be extensively trained and validated to guarantee safe human transportation. Any small errors in the system functionality may lead to fatal accidents and may endanger human lives. Deep learning methods are widely used for environment perception and prediction of hazardous situations. These techniques require huge amount of training data with both normal and abnormal samples to enable the vehicle to avoid a dangerous situation.
The goal of this thesis is to generate simulations from real-world tricky collision scenarios for training and testing autonomous vehicles. Dashcam crash videos from the internet can now be utilized to extract valuable collision data and recreate the crash scenarios in a simulator. The problem of extracting 3D vehicle trajectories from videos recorded by an unknown monocular camera source is solved using a modular approach. The framework is divided into two stages: (a) extracting meaningful adversarial trajectories from short crash videos, and (b) developing methods to automatically process and simulate the vehicle trajectories on a vehicle simulator.Dissertation/ThesisVideo DemonstrationMasters Thesis Computer Science 201
- …