Recent technological advances in multi-touch, multi-display computing interfaces and networking software have enabled the integration of all three roles into a single collaborative work space. This paper describes the development of one such application focused at carrying out complex urban design exercises through simple, transparent, and interactive means. Constructed through a federated system architecture, the system connects three independent applications: Google Earth, a Building Information Model (BIM) database, and an Indicators Dashboard (ID). The goal of this work is to provide a diverse group of stakeholders with a better understanding of the environmental impact and tradeoffs associated with a range in potential urban futures.

A collaborative multi-touch, multi-display, urban futures tool

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications. 

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The examples of the present invention relates to a mapping server and a mapping method for inter-working between a display controlling computer and portable terminal devices. According to an example of the present invention, a mapping server is configured to store information on terminal call signal displayed on a display area of a display unit controlled by a display controlling computer identified on a network, the information on terminal call signal including color information; receive response signal of a portable terminal device generated by the portable terminal device recognizing the terminal call signal displayed on the display area by the display controlling computer through a photo-sensor installed on the portable terminal device; and map between the terminal call signal and the response signal, thereby linking between the display controlling computer and the portable terminal device on the network.

Mapping server and mapping method

SketchBoard: Conceptualizing Interactive Communication Media for the Design Review Process

Tomorrow's immersive applications will leverage Mixed Reality interfaces accessing a multitude of services from distributed clouds. They will face extreme latency constraints, massive datasets, spontaneous collaboration, and constant service churn. This paper outlines our experience evolving an application designed to support collaborative work in Urban Design (UD) practices. The application, UD Co-Spaces, recently weathered significant churn as a core service was discontinued and replaced by a service with a subtly different API. A “dumb pipes” approach, where services communicate through a simple message queue, facilitated this evolution with relatively little disruption to the rest of the system. We show how this strategy can be used to reintroduce new features to the system, and is sustainable as the system's interfaces evolve to use Virtual, Augmented and Mixed Reality environments.

Dumb pipes for smart systems: How tomorrow's applications can salvage yesterday's plumbing

A multi-display collaborative urban planning system with a federated architecture

Aiming at the influence of fewer feature points and dynamic obstacles on location and mapping in off-road environments, we propose a dual-constraint LiDAR-based Simultaneous Localization and Mapping (SLAM) scheme. By abstracting LiDAR registration into two constraints, namely, in-window constraints and out-of-window constraints, the in-window constraints allow our scheme to compromise between accuracy and real-time performance, and out-of-window constraints can exploit optimized variables to provide richer constraint information. The advantages of incremental SLAM map construction can be used to design a variety of map forms. Although the variables outside the window are no longer involved in the optimization, we can use the two-dimensional probability grid map to provide binary semantic information and dynamic weights for the constraints outside the window to enhance the registration accuracy. Finally, we conducted experiments in off-road environment and compared with the mainstream LiDAR SLAM scheme, which can prove that our scheme has more advantages in accuracy.

https://www.mdpi.com/2072-4292/14/22/5705/pdf?version=1668162980

Dual-Constraint Registration LiDAR SLAM Based on Grid Maps Enhancement in Off-Road Environment

This paper proposes a reference path generation method for Autonomous Vehicles(AVs) in the off-road environment. Since obtaining sufficient and reliable prior information in an off-road environment is difficult, we adopt the Astar strategy to complete global routing based on the satellite topology map. Then planning result is corrected based on vehicle localization to obtain the initial reference path. Because the road geometry changes according to the road environment, we will use the centerline of the drivable area extracted with Probabilistic Neural Networks(PNN) to generate the final reference path. While the drivable area is invalid, the reference path planner is applied based on the initial reference path to form the final reference path. In the real off-road scenarios, the proposed method is tested on our experimental vehicle. Experimental results demonstrated that the proposed algorithm could generate reliable reference paths effectively and improve the performance and robustness of local trajectory planning.

/pdf/reference-path-generation-for-autonomous-driving-in-off-road-3dr0fbk2.pdf

Reference Path Generation for Autonomous Driving in Off-road Environment

Accurate annotation of construction disturbance outline greatly influences the evaluation quality in interpretation application. While the loss functions used in semantic segmentation nowadays are difficult to reflect the contour information. Therefore, this paper proposes a RA U-Net(Residual Attention U-Net) construction disturbance interpretation. The model uses an exactly defined Contour Loss(CL) as the loss function. This function could reflect the local segmentation information of each element by extracting and transferring contour features into the weight matrix. Moreover, this paper modifies the CL function to make it suitable for construction disturbance interpretation. The experiments on M-nih Massachusetts Building Dataset and Construction Disturbance Dataset labeled manually show the capability of the proposed function in remote sensing interpretation. The proposed model has been used for the Soil and Water Conservation project supervised by the local government. And the IoU increased 1.85% maximum than binary cross-entropy(BCE) on Mnih Massachusetts Building Dataset.

Using Contour Loss Constraint in Satellite Image Interpretation of Construction Disturbance

Images registration for an optical and synthetic aperture radar (SAR) is a key of multisensor images analysis. Also, parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on scale-invariant feature transform (SIFT) is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling [scale-constrained fast sample consensus (FSC)] is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate (CMR), and root mean square error (RMSE). The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.

Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Tao Su

Author Tools

Chat about Author