944 research outputs found
Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras.
Given a fleet of robots, automatic estimation of the relative poses between them could be inaccurate in specific environments. We propose a framework composed by the fleet of robots with embedded stereoscopic cameras providing 2D and 3D images of the scene, a human coordinator and a Human-Machine Interface. We suppose auto localising each robot through GPS or landmarks is not possible. 3D-images are used to automatically align them and deduce the relative position between robots. 2Dimages are used to reduce the alignment error in an interactive manner. A human visualises both 2D-images and the current automatic alignment, and imposes a new alignment through the Human-Machine Interface. Since the information is shared through the whole fleet, robots can deduce the position of other ones that do not visualise the same scene. Practical evaluation shows that in situations where there is a large difference between images, the interactive processes are crucial to achieve an acceptable result
A comparison of feature extractors for panorama stitching in an autonomous car architecture.
Panorama stitching consists on frames being put together to create a 360o view. This technique is proposed for its implementation in autonomous vehicles instead of the use of an external 360o camera, mostly due to its reduced cost and improved aerodynamics. This strategy requires a fast and robust set of features to be extracted from the images obtained by the cameras located around the inside of the car, in order to effectively compute the panoramic view in real time and avoid hazards on the road. In this paper, we compare and discuss three feature extraction methods (i.e. SIFT, BRISK and SURF) for image feature extraction, in order to decide which one is more suitable for a panorama stitching application in an autonomous car architecture. Experimental validation shows that SURF exhibits an improved performance under a variety of image transformations, and thus appears to be the most suitable of these three methods, given its accuracy when comparing features between both images, while maintaining a low time consumption. Furthermore, a comparison of the results obtained with respect to similar work allows to increase the reliability of our methodology and the reach of our conclusions
Pose Detection and control of multiple unmanned underwater vehicles using optical feedback
This paper proposes pose detection and control algorithms in order to control the relative pose between two Unmanned Underwater Vehicles (UUVs) using optical feedback. The leader UUV is configured to have a light source at its crest which acts as a guiding beacon for the follower UUV which has a detector array at its bow. Pose detection algorithms are developed based on a classifier, such as the Spectral Angle Mapper (SAM), and chosen image parameters. An archive look-up table is constructed for varying combinations of 5-degree-of-freedom (DOF) motion (i.e., translation along all three coordinate axes as well as pitch and yaw rotations). Leader and follower vehicles are simulated for a case in which the leader is directed to specific waypoints in horizontal plane and the follower is required to maintain a fixed distance from the leader UUV. Proportional-Derivative (PD) control (without loss of generality) is applied to maintain stability of the UUVs to show proof of concept. Preliminary results indicate that the follower UUV is able to maintain its fixed distance relative to the leader UUV to within a reasonable accuracy
Online Mapping and Perception Algorithms for Multi-robot Teams Operating in Urban Environments.
This thesis investigates some of the sensing and perception challenges faced
by multi-robot teams equipped with LIDAR and camera
sensors. Multi-robot teams are ideal for deployment in large,
real-world environments due to their ability to parallelize exploration,
reconnaissance or mapping tasks.
However, such domains also impose additional requirements, including the
need for a) online algorithms (to eliminate stopping and waiting for
processing to finish before proceeding) and b) scalability (to handle
data from many robots distributed over a large area).
These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent
maps covering the explored area, 2) online estimation of communication properties
in the presence of buildings and other interfering structure, and 3)
online fusion and segmentation of multiple sensors to aid in object detection.
The contribution of this thesis is the introduction of novel
approaches that leverage grid-maps and sparse multi-variate gaussian
inference to augment the capability of multi-robot teams operating in
urban, indoor-outdoor environments by improving the state of the art
of map rasterization, signal strength prediction, colored point cloud
segmentation, and reliable camera calibration.
In particular, we introduce a map rasterization technique for large
LIDAR-based occupancy grids that makes online updates possible when
data is arriving from many robots at once. We also introduce new
online techniques for robots to predict the signal strength to their
teammates by combining LIDAR measurements with signal strength
measurements from their radios. Processing fused LIDAR+camera point
clouds is also important for many object-detection pipelines. We
demonstrate a near linear-time online segmentation algorithm to this
domain. However, maintaining the calibration of a fleet of 14 robots
made this approach difficult to employ in practice.
Therefore we introduced a robust and repeatable
camera calibration process that grounds the camera model uncertainty in pixel
error, allowing the system to guide novices and experts alike to reliably produce accurate calibrations.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113516/1/jhstrom_1.pd
Flexible Supervised Autonomy for Exploration in Subterranean Environments
While the capabilities of autonomous systems have been steadily improving in
recent years, these systems still struggle to rapidly explore previously
unknown environments without the aid of GPS-assisted navigation. The DARPA
Subterranean (SubT) Challenge aimed to fast track the development of autonomous
exploration systems by evaluating their performance in real-world underground
search-and-rescue scenarios. Subterranean environments present a plethora of
challenges for robotic systems, such as limited communications, complex
topology, visually-degraded sensing, and harsh terrain. The presented solution
enables long-term autonomy with minimal human supervision by combining a
powerful and independent single-agent autonomy stack, with higher level mission
management operating over a flexible mesh network. The autonomy suite deployed
on quadruped and wheeled robots was fully independent, freeing the human
supervision to loosely supervise the mission and make high-impact strategic
decisions. We also discuss lessons learned from fielding our system at the SubT
Final Event, relating to vehicle versatility, system adaptability, and
re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge,
Advancement and Lessons Learned from the Final
Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions
Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future
Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments
GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.This work has been partially funded by the Basque Government, Spain, grant number IT900-16, and the Spanish Ministry of Economy and Competitiveness (MINECO), grant number RTI2018-093337-B-I00
Cooperative Vehicle Perception and Localization Using Infrastructure-based Sensor Nodes
Reliable and accurate Perception and Localization (PL) are necessary for safe intelligent transportation
systems. The current vehicle-based PL techniques in autonomous vehicles are vulnerable to occlusion
and cluttering, especially in busy urban driving causing safety concerns. In order to avoid such safety
issues, researchers study infrastructure-based PL techniques to augment vehicle sensory systems.
Infrastructure-based PL methods rely on sensor nodes that each could include camera(s), Lidar(s),
radar(s), and computation and communication units for processing and transmitting the data. Vehicle
to Infrastructure (V2I) communication is used to access the sensor node processed data to be fused with
the onboard sensor data.
In infrastructure-based PL, signal-based techniques- in which sensors like Lidar are used- can provide
accurate positioning information while vision-based techniques can be used for classification.
Therefore, in order to take advantage of both approaches, cameras are cooperatively used with Lidar in
the infrastructure sensor node (ISN) in this thesis. ISNs have a wider field of view (FOV) and are less
likely to suffer from occlusion. Besides, they can provide more accurate measurements since they are
fixed at a known location. As such, the fusion of both onboard and ISN data has the potential to improve
the overall PL accuracy and reliability.
This thesis presents a framework for cooperative PL in autonomous vehicles (AVs) by fusing ISN
data with onboard sensor data. The ISN includes cameras and Lidar sensors, and the proposed camera Lidar fusion method combines the sensor node information with vehicle motion models and kinematic
constraints to improve the performance of PL. One of the main goals of this thesis is to develop a wind induced motion compensation module to address the problem of time-varying extrinsic parameters of
the ISNs. The proposed module compensates for the effect of the motion of ISN posts due to wind or
other external disturbances. To address this issue, an unknown input observer is developed that uses
the motion model of the light post as well as the sensor data.
The outputs of the ISN, the positions of all objects in the FOV, are then broadcast so that autonomous
vehicles can access the information via V2I connectivity to fuse with their onboard sensory data through
the proposed cooperative PL framework. In the developed framework, a KCF is implemented as a
distributed fusion method to fuse ISN data with onboard data. The introduced cooperative PL
incorporates the range-dependent accuracy of the ISN measurements into fusion to improve the overall
PL accuracy and reliability in different scenarios. The results show that using ISN data in addition to onboard sensor data improves the performance and reliability of PL in different scenarios, specifically
in occlusion cases
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