C_ALL: Cognitive Assistance with LIDAR Localization

Project Overview

C_ALL is an innovative assistive technology system designed to help visually impaired individuals navigate indoor environments safely and independently. The system combines LIDAR-based localization with cognitive assistance to provide real-time guidance, obstacle detection, and spatial awareness to users.

Overview of the C_ALL system architecture and components.

Core Features

LIDAR-Based Localization

• High-precision indoor positioning
• Real-time environment mapping
• Dynamic obstacle detection
• Path planning and navigation
• 3D spatial awareness

LIDAR-generated map showing environment detection and obstacle identification.

Cognitive Assistance

• Voice-based navigation guidance
• Contextual environment descriptions
• Obstacle proximity alerts
• Safe path recommendations
• Emergency assistance features

User interface showing cognitive assistance features and navigation guidance.

Technical Implementation

Sensor Integration

• LIDAR sensor array configuration
• Sensor fusion algorithms
• Real-time data processing
• Environmental mapping
• Position tracking

Detailed view of the sensor integration and data flow.

Navigation System

• Path planning algorithms
• Obstacle avoidance
• Dynamic route adjustment
• User position tracking
• Safety protocol implementation

Navigation system architecture and path planning visualization.

User Interface

Voice Interface

• Natural language processing
• Context-aware commands
• Real-time feedback
• Emergency protocols
• User customization options

Voice interface system and user interaction flow.

Safety Features

• Collision prevention
• Emergency stop protocols
• Battery monitoring
• System health checks
• Backup navigation modes

Overview of safety features and emergency protocols.

Performance Analysis

• Navigation accuracy metrics
• Response time measurements
• Battery life optimization
• System reliability tests
• User feedback integration

System performance analysis and optimization results.

Future Developments

Planned enhancements include:

• Enhanced object recognition
• Improved battery efficiency
• Expanded environment mapping
• Multi-user support
• Cloud-based data processing
• Integration with smart building systems

Project Repository

You can find the full project on GitHub.