LiDAR for Autonomous Robotics: Physics, Systems, and Assurance provides a rigorous, systems-level guide to designing LiDAR for real autonomous applications. Beginning with optical propagation and photodetection, it builds from first principles through ranging, scanning, point-cloud processing, localization, sensor fusion, tracking, and machine-learning perception-then extends the discussion into adversarial robustness, verification, validation, safety, regulation, and certification. Written for practicing engineers and graduate students, the book emphasizes not simply how LiDAR works, but how to make and defend real engineering decisions. Civil robotaxi and defense counter-UAS examples throughout connect the underlying physics to practical system design, performance limits, operational risk, and assurance. From Maxwell's equations to the safety case, LiDAR for Autonomous Robotics offers a unified foundation for building autonomous sensing systems whose performance can be predicted, tested, and trusted.