This Slam And Localization eBook from 3D-LABS is written for engineers and students who want a clear, practical reference they can apply immediately.
SLAM and Localization — Practical Implementations covers EKF-SLAM, FastSLAM, Graph-SLAM, and visual SLAM algorithms with Python/C++ implementations and benchmarks on KITTI, EuRoC, and ICL-NUIM datasets.
What format does this book follow?
Algorithm-to-implementation-to-benchmark structure: each SLAM variant derived mathematically, implemented in Python/C++ on GitHub, and benchmarked on public datasets. References: Thrun et al. Probabilistic Robotics (2005), IEEE RA-L/IROS, and ROS 2 Humble REP-2000.
What is the EKF-SLAM prediction equation?
State prediction: x_hat_(k|k-1) = f(x_hat_(k-1), u_k), P_(k|k-1) = F_k*P_(k-1)*F_k^T + Q_k. Update: K = P*H^T*(H*P*H^T+R)^-1. State vector: 3+2N dimensions for robot pose plus N landmark positions.
What visual SLAM methods are covered?
ORB-SLAM3 (ORB descriptors, essential matrix 5-point algorithm), LSD-SLAM (photometric residual minimisation), DROID-SLAM (deep learning recurrent optical flow + bundle adjustment). LiDAR: LOAM and LeGO-LOAM. All include loop closure with g2o or GTSAM pose graph optimisation.
How do I access this book after purchase?
eBook PDF: Instant download via email and 3D-LABS dashboard. Paper Book: Printed and shipped to India.
What’s Included
An instant PDF download covering the core concepts, practical examples, and key references — ready to read on any device.
