#Data Fusion

This guide explores using the Error-State Kalman Filter (ESKF) to merge IMU and GPS data, offering improved positional precision by minimizing errors present in IMU-only outcomes. It includes comprehensive instructions for implementing ESKF, enumerating required dependencies such as Eigen, Yaml, and Glog. The process of compiling and executing the eskf-gps-imu-fusion project is also described. Visualizing trajectories and conducting error analysis with evo are discussed, along with integrating other data sources for algorithm compliance. Future updates will focus on enhancing gravity alignment and bias estimation during initialization.

Examine a comprehensive benchmark for multimodal learning, covering diverse datasets, modalities, and prediction tasks across numerous research areas. The benchmark evaluates performance and robustness and provides innovative tools to support research in fields like multimedia and healthcare. Learn about MultiZoo’s methods designed for accessibility and reproducibility in multimodal learning.