#Kalman Filter

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.

This resource offers a detailed overview of inertial navigation, laser mapping, and odometry, combining theoretical concepts with practical code applications. Known for simplifying mathematical derivations and code implementations, it allows for the replication of classic laser SLAM algorithms and structures. Key subjects include Kalman filter integration with IMU and GNSS, navigation pre-integration, 2D and 3D laser mapping techniques, as well as efficient LiDAR odometry using ICP and NDT algorithms. It also covers the role of concurrent programming in real-time positioning, supported by dynamic demonstrations and comprehensive datasets. This is a valuable resource for understanding SLAM technology, particularly in the context of autonomous driving, without overcomplication.

Discover a framework tailored for visual odometry and sensor fusion localization with advanced Kalman filter techniques, including symbolic Jacobian computations, error state filters for 3D orientation, and Multi-State Constraint Kalman Filters. The library also offers RTS smoothing for offline estimation and a Mahalanobis distance-based outlier rejection to improve measurement accuracy, featuring a straightforward Python implementation.

This collection of math and physics algorithms in Unity's C# offers diverse tools. It includes mesh-deformation techniques, the Kabsch algorithm for rotation and translation, and Kalman filtering for data refinement. Simulations are enhanced with Verlet integration and inverse kinematics constraints. Solve problems using Nelder-Mead methods and improve collision detection with spatial hashing. From mesh deformation to JSON serialization, this toolkit supports versatile Unity projects.