PythonRobotics is a large collection of robotics algorithms implemented in Python, designed primarily for educational purposes and as a companion to a textbook. It covers a wide range of robotics domains including localization (EKF, particle filter, histogram filter), mapping (grid maps, NDT, clustering), SLAM (EKF SLAM, FastSLAM, GraphSLAM), path planning (A*, Dijkstra, RRT variants, PRM, DWA, Frenet, etc.), path tracking (Stanley, LQR, MPC, pure pursuit), arm navigation (inverse kinematics, RRT for arms), aerial navigation (drone trajectory, rocket landing), bipedal walking, inverted pendulum control, and mission planning (state machines, behavior trees). Each algorithm is implemented as a standalone script with a main() function that runs a simulation and visualizes results using matplotlib. The code is not packaged as a library and is not intended for production use; it is a learning resource with clear, readable implementations that reference academic papers. The repository includes extensive documentation in Sphinx RST format, CI/CD pipelines, and a test suite that runs each demo without animation.
robot_localization is a ROS 2 package providing nonlinear state estimation for mobile robots using Extended (EKF) and Unscented Kalman Filters (UKF). It fuses multiple sensor inputs—including odometry, IMU, GPS, pose, and twist measurements—into a consistent, filtered pose estimate (position, orientation, velocities, accelerations) published as `/odometry/filtered` and broadcast as TF transforms following REP-105 conventions. The architecture is centered around a templated `RosFilter<T>` class that wraps the core `FilterBase` abstract class, handling measurement queuing, differential/relative modes, Mahalanobis outlier rejection, and diagnostic monitoring. Key entry points are `ekf_node`, `ukf_node`, and `navsat_transform_node` (for GPS-to-local Cartesian conversion), all instantiated from `ros_filter.cpp` and `navsat_transform.cpp`. The package also provides services like `SetPose`, `SetDatum`, `ToLL`, and `GetState`, and includes lifecycle node support for managed operation. The codebase is well-known for its extensive parameterization—each sensor input can be individually configured with update vectors, differential/relative flags, noise terms, and rejection thresholds—making it adaptable to diverse robot platforms. It depends on Eigen for linear algebra, GeographicLib for geodetic conversions, and standard ROS 2 infrastructure (tf2, diagnostic_updater). The core filter logic (`FilterBase`) and coordinate conversion utilities (`navsat_conversions.hpp`) are clean, isolated components that can be reused outside the ROS ecosystem. Despite its popularity and solid engineering, the repository shows signs of technical drift: incomplete ROS 1→ROS 2 migration, deprecated API usage (`rclcpp::Duration::from_seconds`), commented-out bag-based tests, and a monolithic `RosFilter` implementation that mixes configuration, callbacks, and filter logic. Issue templates explicitly state that the package will soon be deprecated and feature requests will not be addressed, indicating transition to a maintenance-only state.