Autonomous Navigation & SLAM
Develop algorithms that allow mobile robots to map their environment and navigate autonomously. Focus on Simultaneous Localization and Mapping (SLAM), path planning, and obstacle avoidance.
15 courses
Program autonomous self-driving robots using ROS 2 with Python and C++ to master mapping, localization, and SLAM algorithms through practical, text-based guides.
Learn how self-driving cars track their position and motion using sensor fusion, Kalman filters, and modern state estimation algorithms.
Build a foundation in autonomous vehicle logic by learning how to integrate sensor data for precise navigation and strategic flight planning.
Master the fundamentals of state estimation to build robust tracking and navigation systems using linear, nonlinear, and particle filtering techniques.
Master Bayesian tracking and non-linear Kalman filters to build reliable perception systems for self-driving cars and driver assistance technologies.
Master the fundamental mathematics and programming logic behind vision-based navigation, motion estimation, and mapping for self-driving cars and drones.
Learn the fundamentals of autonomous marine vehicles, underwater acoustic communications, and navigation systems through clear written explanations and simulated scenarios.
Learn how to adapt the classic Sidewinder algorithm in Ruby to generate custom mazes with unbroken southern corridors using clean, modern coding practices.
Master the fundamentals of state estimation and sensor fusion to design, implement, and analyze Kalman filters for tracking and navigation systems.
Master the foundational principles of rigid body dynamics, stability theory, and modern trajectory planning to design control systems for autonomous aircraft and spacecraft.
Learn to combine data from multiple sensors to accurately detect, classify, and track targets using modern state estimation and fusion algorithms.
Learn how to position 3D assets, align objects to spatial grids, and implement intelligent tracking solvers in Unity for immersive experiences.
Master the fundamentals of particle filtering and state estimation to track and localize systems in non-linear environments using step-by-step written guides and Python.
Master the mathematics and implementation of linear state estimation to track moving objects with precision.
Master the mathematics and implementation of Extended and Unscented Kalman filters to estimate states and parameters in real-world nonlinear systems.