Sensorfusionsramverk

A sensorfusionsramverk is a software framework designed to integrate data from multiple sensors into a single, coherent estimate of a system’s state or its environment. It provides abstractions for sensor interfaces, time synchronization, calibration data, data processing pipelines, and a modular fusion core that can host various fusion algorithms. The goal is to improve accuracy, robustness, and situational awareness by leveraging complementary information from diverse sensors.

The typical architecture includes sensor drivers and data streams, time stamping and synchronization mechanisms, calibration management, preprocessing and filtering stages, and a fusion engine that maintains a state representation. A common design uses a modular plug-in system for sensors and for different fusion algorithms, along with configuration, logging, and visualization tools. Real-time or near-real-time execution is often required, with attention to deterministic scheduling and thread safety.

Algorithms employed in sensorfusionsramverk range from classical statistical methods to modern optimization-based approaches. Kalman filter families, such as the Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF), are widely used for linear or mildly nonlinear systems. Complementary filters, particle filters, and factor-graph or optimization-based fusion methods are also common. In practice, a framework may support multiple algorithms and allow switching based on sensor models, reliability, and latency requirements. State representations typically include pose, velocity, and sensor biases, with measurement models that capture sensor noise and correlations.

Applications span robotics, autonomous vehicles, drones, augmented/virtual reality, and industrial automation. Sensorfusionsramverk are used to fuse data from IMUs, GNSS, cameras, LiDAR, radar, and other modalities to produce accurate localization, mapping, and perception. Design considerations include latency, synchronization accuracy, calibration maintenance, testing and verification, and integration with simulation and higher-level decision systems. Open-source examples in related ecosystems include ROS packages that perform multi-sensor fusion and state estimation, along with libraries that support factor graphs and optimization-based fusion.