State Estimation
Showing 12 of 12 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.
A robust visual-inertial odometry framework for real-time motion estimation using cameras and IMUs.
A deep learning-enhanced Kalman filter for accurate vehicle dead reckoning using only an IMU sensor.
A ROS framework for sensor fusion using nonlinear least squares optimization, enabling state estimation, localization, mapping, and calibration on robots.
A lightweight, accurate, and robust monocular visual-inertial odometry system based on a hybrid Multi-State Constraint Kalman Filter.
A recursive B-spline-based state estimation framework for 6-DoF LiDAR odometry, supporting LiDAR-only, LiDAR-inertial, and multi-LiDAR configurations.
A Go library implementing state estimation and filtering algorithms including Kalman, Extended Kalman, Unscented Kalman, and Particle filters.
A JavaScript implementation of the Kalman filter for state estimation in noisy systems.
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