APPLIED PROBABILISTIC ROBOTICS

YOUR ROBOT WORKS PERFECTLY IN SIMULATION. SO WHY DOES IT FAIL IN THE REAL WORLD?

Sensors lie. Wheels slip. IMUs drift. LiDAR can misread the environment. Your elegant equations can behave very differently when deployed on physical hardware.

Applied Probabilistic Robotics shows you how to build autonomous robots that can reason through that uncertainty.

Instead of leaving you buried in academic theory-or handing you simplified code that collapses outside the simulator-this practical guide bridges the gap between probabilistic robotics theory and production-ready autonomous systems.

You'll learn how to master robot localization, SLAM, sensor fusion, state estimation, Bayesian inference, autonomous navigation, motion models, sensor models, and probabilistic decision-making while connecting the mathematics directly to real software architecture and implementation.

You'll also explore Extended Kalman Filters (EKF), covariance, nonlinear optimization, factor graphs, C++, Eigen, GTSAM, and ROS 2, including the engineering realities that textbooks often overlook: asynchronous sensor data, dropped network packets, calibration, memory constraints, covariance tuning, filter divergence, and the notorious Sim-to-Real gap.

Every major concept follows a practical path:

Physical Problem → Mathematical Model → Software Architecture → C++ Implementation → Hardware Reality.

Whether you're an autonomy engineer, robotics software developer, graduate student, or engineer ready to move beyond toy simulations, this book gives you the framework to turn noisy sensor data and uncertain environments into reliable robotic intelligence.

Stop programming robots as if the physical world were perfect. Learn to make uncertainty work for you.

Get your copy of Applied Probabilistic Robotics and start building autonomy that can survive reality.