3,872 research outputs found
Towards Sensor Enhanced Virtual Reality Teleoperation in a Dynamic Environment
A teleoperation interface is introduced featuring an integrated virtual reality based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The virtual reality system addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. It also enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. Finally, the system provides real time update to the virtual environment based on anomalies encountered by the vehicle. System architecture and preliminary implementation results are discussed, and future work focused on incorporating dynamic moving objects in the environment is described
Sensor Augmented Virtual Reality Based Teleoperation Using Mixed Autonomy
A multimodal teleoperation interface is introduced, featuring an integrated virtual reality (VR) based simulation augmented by sensors and image processing capabilities onboard the remotely operated vehicle. The proposed virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multimodal control interface. VR addresses the typical limitations of video based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an onboard computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and a real-state tracking system enable temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. The system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The VR based multimodal teleoperation interface is expected to be more adaptable and intuitive when compared with other interfaces
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Embarking on the Autonomous Journey: A Strikingly Engineered Car Control System Design
openThis thesis develops an autonomous car control system with Raspberry Pi. Two predictive models are implemented: a convolutional neural network (CNN) using machine learning and an input-based decision tree model using sensor data. The Raspberry Module controls the car hardware and acquires real-time camera data with OpenCV. A dedicated web server and event stream processor process data in real-time using the trained neural network model, facilitating real-time decision-making. Unity and Meta Quest 2 VR set create the VR interface, while a generic DIY kit from Amazon and Raspberry PI provide the car hardware inputs. This research demonstrates the potential of VR in automotive communication, enhancing autonomous car testing and user experience.This thesis develops an autonomous car control system with Raspberry Pi. Two predictive models are implemented: a convolutional neural network (CNN) using machine learning and an input-based decision tree model using sensor data. The Raspberry Module controls the car hardware and acquires real-time camera data with OpenCV. A dedicated web server and event stream processor process data in real-time using the trained neural network model, facilitating real-time decision-making. Unity and Meta Quest 2 VR set create the VR interface, while a generic DIY kit from Amazon and Raspberry PI provide the car hardware inputs. This research demonstrates the potential of VR in automotive communication, enhancing autonomous car testing and user experience
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
Real-time computation of distance to dynamic obstacles with multiple depth sensors
We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task
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