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Case Study

How Voidmapper Reshapes the Mining Industry with Real-Time Point Cloud Processing

About Voidmapper

Voidmapper stands as a game-changer in the mining industry, offering a comprehensive solution designed to streamline point cloud data handling. Its focus lies in deformation detection and ongoing tracking, ensuring timely and accurate reporting through the PSP portal.

By leveraging Voidmapper’s capabilities, mining operations experience significant reductions in time and cost, coupled with enhanced data accuracy and reliability. Voidmapper’s innovation unlocks the full potential of LiDAR technology, setting new standards for safety and productivity in underground mining processes.

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The Challenge

Voidmapper confronts the challenge of bridging the gap between raw point cloud data acquisition and actionable insights for mine operators. Leveraging the PSP portal as a frontend, Voidmapper enables clients to visualize intricate point cloud images and initiate complex processing tasks. However, this integration introduces complexities, especially in handling and processing voluminous point cloud datasets in a timely, efficient, and accurate manner.

Key technical challenges include:

  • Handling Large-Scale Point Cloud Data: Processing 3D scans of mine tunnels, each ranging from 1.2GB to 2GB, presents significant computational and storage challenges.
  • Event-Driven Processing: Swift reaction to new data uploads requires a robust, event-driven architecture to automate processing tasks.
  • Scalability and Resource Management: Initial limitations with a single EC2 instance underscore the need for scalable solutions to meet growing demand.
  • Sequential and Parallel Job Orchestration: Orchestrating processing jobs concurrently adds complexity, especially for maintaining chronological integrity in deformation tracking.
  • Scalable and Efficient AWS Batch Jobs: Ensuring scalability and cost-effectiveness in AWS Batch processing to handle spikes in demand without delays is critical.
  • Time-Sensitive PDF Report Generation: Prompt generation of accurate reports post-processing is crucial, despite concurrent tasks.
  • Integration Complexity: Integrating seamlessly with PSP introduces complexities, requiring meticulous attention to data integrity and secure API communications.

The Solution

The architecture of the Voidmapper project was meticulously crafted to cater to the specific demands of high-volume, real-time point cloud data processing, leveraging a suite of AWS services. Various AWS services were employed to ensure seamless data handling and processing. These included Amazon S3, a robust storage solution for point cloud files, and AWS Lambda, a serverless execution model to trigger processing tasks. AWS Step Functions, chosen for their ability to orchestrate complex workflows,  manage the sequence of processing steps. AWS Batch with AWS Fargate were chosen to handle high-throughput workloads efficiently.

Additionally, AWS API Gateway provided a robust, scalable front door facilitating secure and efficient communication between the PSP portal and Voidmapper backend APIs. DynamoDB, an efficient NoSQL database service, was utilized for quick retrieval and update of job statuses and metadata, crucial for managing the state of processing jobs.

These services, along with others like Amazon EventBridge, AWS CodeBuild, Amazon CloudWatch, and AWS IAM, were integrated to construct a cohesive system capable of efficiently managing and scaling with the processing demands, ensuring timely and accurate point cloud data analysis for Voidmapper’s clients.

In addition to AWS services, the solution integrates with the PSP portal, serving as the front-end application for Voidmapper. This integration plays a central role in the project, utilizing PSP as both an entry point for processing requests and a platform for delivering results. By leveraging PSP’s capabilities, Voidmapper ensures seamless file viewing, processing triggers, and results presentation, enhancing the overall user experience and functionality of the system.

List of AWS Services Used:

  • AWS S3 (Simple Storage Service)
  • AWS Lambda
  • AWS Step Functions
  • AWS Batch with AWS Fargate
  • AWS API Gateway
  • Amazon DynamoDB
  • Amazon EventBridge
  • AWS CodeBuild and Amazon Elastic Container Registry (ECR)
  • AWS CloudWatch
  • Amazon Route 53 and AWS Certificate Manager
  • AWS Simple Queue Service (SQS)
  • Amazon EC2

The Result

By leveraging AWS services and addressing complex challenges, Voidmapper has redefined mining operations with real-time point cloud processing, setting new standards for safety and efficiency in the industry.

Processing Time: Voidmapper transitioned from labor-intensive, days-long processing to a much faster and efficient system powered by AWS Batch. This transition reduced the image processing time from days to just a couple of hours, allowing for quicker access to critical data and insights.

Operational Scalability: By migrating from a single EC2 instance to a scalable AWS Batch environment, Voidmapper gained the ability to dynamically handle increased workloads. This improved scalability ensures that the system can accommodate growing demands without encountering resource constraints, thereby enhancing operational efficiency.

Resource Utilization: Implementation of AWS Fargate within AWS Batch significantly improved resource allocation efficiency. With Fargate’s capability to automatically adjust resources based on job requirements, Voidmapper ensured optimal utilization of resources without over-provisioning, leading to cost savings and improved performance.

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