4.3 Sensor fusion and data processing

Sensor fusion combines data from multiple sensors to create a more accurate understanding of a robot's environment. By leveraging different sensor types, it improves perception, enhances reliability, and compensates for individual sensor limitations.

Implementing sensor fusion involves algorithms like Kalman filters and particle filters, along with signal processing techniques. The challenge lies in balancing complexity and performance, optimizing for accuracy, processing time, and resource constraints in embedded systems.

Sensor Fusion Fundamentals

Concept of sensor fusion

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• Sensor fusion combines data from multiple sensors creating more accurate comprehensive understanding of environment
• Improves robotic perception leveraging strengths of different sensor types enhances reliability through redundancy compensates for individual sensor limitations
• Types include complementary fusion competitive fusion cooperative fusion
• Common sensors cameras (visual data) LiDAR (distance measurements) IMU (inertial data) GPS (global positioning)
• Benefits reduced uncertainty in measurements extended range of operating conditions improved obstacle detection and avoidance
• Applications autonomous navigation object recognition and tracking localization and mapping (SLAM)

Sensor Fusion Algorithms and Data Processing

Implementation of fusion algorithms

• Kalman filter linear quadratic estimation algorithm uses prediction-correction cycle state space model accounts for process and measurement noise
• Extended Kalman filter (EKF) adapts to non-linear systems linearizes through Taylor series expansion
• Unscented Kalman filter (UKF) employs sigma point sampling for non-linear systems
• Particle filter uses Monte Carlo method for non-Gaussian distributions applies resampling techniques
• Multi-sensor fusion architectures centralized decentralized distributed
• Implementation considerations
  1. Synchronize sensors
  2. Associate data
  3. Optimize computational efficiency

Signal processing for sensor data

• Filtering techniques low-pass high-pass band-pass notch filters
• Digital filtering FIR (Finite Impulse Response) IIR (Infinite Impulse Response)
• Noise reduction methods moving average median filters
• Feature extraction edge detection corner detection SIFT (Scale-Invariant Feature Transform) SURF (Speeded Up Robust Features)
• Dimensionality reduction Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA)
• Time-frequency analysis short-time Fourier transform wavelet transform
• Signal enhancement adaptive filtering Wiener filtering

Complexity vs performance in fusion

• Computational complexity time complexity (Big O notation) space complexity real-time processing requirements
• Performance metrics accuracy precision recall F1 score
• Trade-off analysis accuracy vs processing time memory usage vs computation speed sensor resolution vs data processing load
• Optimization techniques algorithm simplification parallel processing hardware acceleration (GPUs FPGAs)
• Scalability issues increasing number of sensors higher sensor data rates
• Resource constraints in embedded systems limited processing power memory limitations power consumption
• Adaptive sensor fusion strategies context-aware sensor selection dynamic algorithm switching based on computational load