2,468 research outputs found
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
LaneMapper: A City-scale Lane Map Generator for Autonomous Driving
Autonomous vehicles require lane maps to help navigate from a start to a goal position in a safe, comfortable and quick manner. A lane map represents a set of features inherent to the road, such as lanes, stop signs, traffic lights, and intersections. We present a novel approach to detect multiple lane boundaries and traffic signs to create a 3D city-scale map of the driving environment. We detect, recognize and track lane boundaries with multimodal sensory and prior inputs, such as camera, LiDAR, and GPS/IMU, to assist autonomous driving. We detect and classify traffic signs from the image considering high reflectivity of LiDAR points and further register the locations of traffic signs and lane boundaries together in the world coordinate frame. We have also made our code base open-source for the research community to tweak or use our algorithm for their purposes
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation
Monocular Re-Localization (MRL) is a critical component in autonomous
applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map
based on monocular images. In recent decades, significant progress has been
made in the development of MRL techniques. Numerous algorithms have
accomplished extraordinary success in terms of localization accuracy and
robustness. In MRL, scene maps are represented in various forms, and they
determine how MRL methods work and how MRL methods perform. However, to the
best of our knowledge, existing surveys do not provide systematic reviews about
the relationship between MRL solutions and their used scene map representation.
This survey fills the gap by comprehensively reviewing MRL methods from such a
perspective, promoting further research. 1) We commence by delving into the
problem definition of MRL, exploring current challenges, and comparing ours
with existing surveys. 2) Many well-known MRL methods are categorized and
reviewed into five classes according to the representation forms of utilized
map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized
semantic map, and neural network-based map. 3) To quantitatively and fairly
compare MRL methods with various map, we introduce some public datasets and
provide the performances of some state-of-the-art MRL methods. The strengths
and weakness of MRL methods with different map are analyzed. 4) We finally
introduce some topics of interest in this field and give personal opinions.
This survey can serve as a valuable referenced materials for MRL, and a
continuously updated summary of this survey is publicly available to the
community at: https://github.com/jinyummiao/map-in-mono-reloc.Comment: 33 pages, 10 tables, 16 figures, under revie
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
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