On the Derivation of Tightly-Coupled LiDAR-Inertial Odometry with VoxelMap
This work serves as a technical reference and accessible entry point for foundational understanding in robotics and autonomous systems, but it is incremental as it does not propose a new algorithm.
The paper tackles the problem of LiDAR-inertial odometry by providing a concise mathematical formulation within an iterated error-state Kalman filter framework using a VoxelMap representation, resulting in a clear and self-contained derivation that unifies geometric modeling and probabilistic state estimation.
This note presents a concise mathematical formulation of tightly-coupled LiDAR-Inertial Odometry within an iterated error-state Kalman filter framework using a VoxelMap representation. Rather than proposing a new algorithm, it provides a clear and self-contained derivation that unifies the geometric modeling and probabilistic state estimation through consistent notation and explicit formulations. The document is intended to serve both as a technical reference and as an accessible entry point for a foundational understanding of the system architecture and estimation principles.