Sensor Fusion Algorithms

- Overview

Sensor fusion algorithms combine data from multiple sensors to reduce uncertainty and provide a more accurate and reliable understanding of the environment. 

Common types include probabilistic filters like the Kalman filter and its variants, Bayesian networks, and more complex machine learning methods like convolutional neural networks. These algorithms process sensor data to improve measurement accuracy, reliability, and coverage for tasks like navigation, object detection, and state estimation.  

- Probabilistic and Filtering Algorithms

• Kalman Filter: A widely used algorithm that recursively estimates the state of a system from a series of noisy measurements. It calculates a "trust level" (Kalman gain) for each sensor's output and adjusts it dynamically. Variations like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used for non-linear systems.
• Complementary Filter: A simpler filter that manually sets the trust level between sensors based on their respective strengths. For example, it might trust a gyroscope for fast-changing orientation but rely on a magnetometer for long-term drift correction.
• Particle Filter: A more computationally intensive method that uses a set of random samples (particles) to approximate the probability distribution of the state. It is effective for non-linear and non-Gaussian problems.
• Bayesian Networks: A graphical model representing probabilistic relationships between variables. It can be used to infer the most likely state based on sensor data and prior knowledge.
• Dempster–Shafer Theory: An alternative to Bayesian methods that deals with uncertainty and ignorance by using belief functions.

- ML Algorithms and Other Methods

• Neural Networks: Can be used for sensor fusion, especially in cases involving image data from multiple sensors (like cameras and LiDAR) to classify objects or make decisions.
• Convolutional Neural Networks (CNNs): A type of neural network well-suited for processing data with a grid-like topology, such as images.

2. Other methods: 

• Central Limit Theorem (CLT): Can be used for simpler averaging of data from multiple sensors, assuming the individual sensor errors are independent.
• Fuzzy Logic: An approach that uses "if-then" rules to process information from different sensors, similar to how humans might reason.

- Levels of Fusion

• Sensor-level (Low-level): Raw data from multiple sensors is combined. This can improve the quality of a single measurement.
• Feature-level (Mid-level): Features are extracted from each sensor's data independently and then combined to form a single feature vector before processing.
• Decision-level (High-level): Each sensor's output is processed to make an independent decision, and these decisions are then combined.