OpenSLAM Platform

High-Level Architecture

```shell

React Client Flask Server SLAM Engine
(TypeScript) (Python) (Python)

            PostgreSQL              MinIO       
                        (Database)           (File Storage) 

```

Features

Core Capabilities

• Algorithm Development Framework

  • Modular architecture for easy algorithm integration
  • Support for Visual SLAM, LiDAR SLAM, and Visual-Inertial SLAM
  • Comprehensive testing and benchmarking tools

• Real-time Visualization

  • Interactive 3D trajectory visualization
  • Point cloud rendering and manipulation
  • Real-time performance metrics

• Dataset Integration

  • Native KITTI dataset support
  • Custom dataset upload and management
  • Automated data preprocessing pipelines

Technical Features

• Frontend

  • React-based responsive web interface
  • Real-time WebSocket data streaming
  • Interactive 3D visualizations using Three.js
  • TypeScript for enhanced type safety

• Backend

  • Flask-based RESTful API
  • Celery for distributed task processing
  • PostgreSQL for structured data storage
  • MinIO for large dataset handling

• SLAM Engine

  • Pure Python implementation
  • NumPy/SciPy optimization
  • CUDA acceleration support
  • Extensible algorithm interface ##

Dataset Processing

mermaid graph TD A[Upload Dataset] --> B[Validate Format] B --> C[Convert to Internal Format] C --> D[Store in MinIO] D --> E[Create Database Entry]

SLAM Execution

mermaid graph TD A[Configure Algorithm] --> B[Initialize Engine] B --> C[Process Frames] C --> D[Update Visualization] D --> E[Store Results]

Getting Started

Prerequisites

```bash

System Requirements

Python 3.8+ Node.js 16+ PostgreSQL 13+ CUDA 11+ (optional, for GPU acceleration)

Required Python packages

numpy>=1.21.0 scipy>=1.7.0 opencv-python>=4.5.0 pytorch>=1.9.0

Frontend dependencies

node>=16.0.0 npm>=7.0.0 ```

Installation

```bash

Clone repository

git clone https://github.com/armanasq/openslam.git cd openslam

Backend setup

python -m venv venv source venv/bin/activate pip install -r requirements.txt

Frontend setup

cd frontend npm install ```

Quick Start

```bash

Start backend server

cd backend flask run

Start frontend development server

cd frontend npm start

Access the platform

open http://localhost:3000 ```

Documentation

Architecture Overview

openslam/ frontend/ # React frontend application backend/ # Flask backend server slam_engine/ # Core SLAM implementation

Key Components

1. Frontend Application

   • User interface and visualization
   • Real-time data streaming
   • Configuration management

2. Backend Server

   • REST API endpoints
   • WebSocket server
   • Task queue management

3. SLAM Engine

   • Algorithm implementations
   • Optimization routines
   • Data processing pipelines

Contributing

We welcome contributions! Please read our Contributing Guidelines before submitting PRs.

Development Setup

1. Fork the repository
2. Create a feature branch
3. Make changes
4. Submit a pull request

Code Standards

• Python: PEP 8
• TypeScript: ESLint configuration
• Documentation: Google style docstrings

Roadmap

Phase 1: Foundation (Q3 2024)

• [x] Core architecture design
• [x] Basic frontend implementation
• [x] Backend API structure
• [ ] Basic SLAM pipeline
• [ ] Initial documentation

Phase 2: Core Features (Q4 2024)

• [ ] Visual SLAM implementation
• [ ] LiDAR SLAM integration
• [ ] Real-time visualization
• [ ] Dataset management

Phase 3: Advanced Features (Q1 2025)

• [ ] Multi-sensor fusion
• [ ] Loop closure detection
• [ ] Performance optimization
• [ ] Community features

Technical Details

Supported Algorithms

• ORB-SLAM3
• VINS-Mono
• LOAM
• CartographerSLAM

Performance Metrics

• Trajectory Accuracy
• Computational Efficiency
• Memory Usage
• Loop Closure Success Rate

Integration Options

```python

Python API Example

from openslam import SLAMEngine

engine = SLAMEngine(config={ 'algorithm': 'orbslam3', 'sensortype': 'stereo' })

results = engine.processsequence(datasetpath) ```

Benchmarks

Detailed benchmarks will be available after first release

Security

Data Privacy

• End-to-end encryption for data transfer
• Secure dataset storage
• Access control mechanisms

Authentication

• JWT-based authentication
• Role-based access control
• OAuth2 integration (planned)

License

Copyright (c) 2024 OpenSLAM Team.

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Acknowledgments

• KITTI Dataset Team
• Open-source SLAM community
• Research institutions and contributors

Contact

• Project Lead: Arman Asgharpoor
• Website: openslam.ai (Coming Soon)