Multi-Modal Localization Framework
GPS is unreliable indoors, necessitating alternative localization approaches. Our multi-modal framework combines visual, WiFi, and IMU sensors for robust indoor positioning.
System Architecture
Our localization system integrates:
Visual Localization
- SLAM-based tracking
- Visual place recognition
- Loop closure detection
WiFi Fingerprinting
- Signal strength mapping
- Access point triangulation
- Machine learning-based positioning
IMU Integration
- Dead reckoning
- Motion model constraints
- Drift correction
Sensor Fusion Strategy
We employ an Extended Kalman Filter (EKF) to fuse multiple modalities:
- Prediction Step: IMU-based motion prediction
- Update Step: Visual and WiFi measurements
- Adaptive Weighting: Dynamic sensor reliability assessment
Implementation Highlights
Key features of our implementation:
- Modular Design: Easy to add/remove sensor modalities
- Real-time Processing: <50ms latency on mobile devices
- Map Building: Simultaneous mapping and localization
- Failure Recovery: Graceful degradation when sensors fail
Performance Evaluation
Testing in various indoor environments:
- Office Building: 0.8m mean error, 95% < 2m
- Shopping Mall: 1.2m mean error, 95% < 3m
- Warehouse: 1.5m mean error, 95% < 3.5m
WiFi significantly improves performance in visually repetitive environments.
Open Source
Our localization framework is available on GitHub with example datasets and documentation.
Conclusion
Multi-modal sensor fusion provides robust indoor localization. The key is adaptive fusion that leverages the strengths of each modality while handling individual sensor failures gracefully.