sharma-n/SLAM

This project is a classic academic/hobbyist repository implementing standard SLAM algorithms that have been well-documented in robotics textbooks (e.g., Thrun's 'Probabilistic Robotics') for nearly two decades. With only 25 stars and no activity in several years (stale velocity), it functions primarily as a personal study guide or educational resource rather than a production-grade tool. It lacks a moat because the algorithms (Extended Kalman Filter, Particle Filters) are commodity knowledge and the implementation platform (MATLAB/Octave) is rarely used for real-time robotics deployment compared to C++ or Python-based frameworks like GTSAM, Ceres Solver, or ROS-integrated libraries. Modern SLAM has moved toward visual-inertial odometry (VIO), LiDAR-based factor graphs, and neural radiance fields (NeRFs), rendering these basic implementations technically obsolete for frontier applications. Displacement is immediate as superior educational resources like 'PythonRobotics' offer more comprehensive and modern alternatives.