State Estimation
Showing 5 of 5 projects
An interactive Jupyter Notebook book teaching Kalman and Bayesian filters through Python code and practical examples.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
An optimization-based multi-sensor state estimator for accurate self-localization in drones, cars, and AR/VR applications.
A ROS package providing nonlinear state estimation nodes for robot localization using sensor fusion.
A realtime LiDAR odometry and mapping (LOAM) method for state estimation and mapping using 3D lidar sensors like Velodyne VLP16.
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