Visual Slam For Robotics: Build A Vslam System In Python

Build a complete Visual SLAM system in Python & ROS 2 — feature tracking, bundle adjustment, loop closure and 3D map

What you'll learn

Build a complete monocular, stereo and RGB-D visual SLAM system in Python from scratch — feature tracking, pose estimation, mapping and loop closure.

Master the geometry behind VSLAM: camera calibration, epipolar constraints, the essential and fundamental matrices, triangulation and PnP pose recovery.

Implement a full back end — keyframe selection, local and global bundle adjustment, pose-graph optimization and drift correction with g2o/GTSAM-style solvers.

Run and tune ORB-SLAM3, RTAB-Map and visual-inertial odometry on real robots in ROS 2, and fuse IMU data for scale and robustness.

Requirements

Basic Python. If you can write a function and a loop, you are ready — every algorithm is built step by step from first principles.

High-school linear algebra is enough. Matrices, vectors and transforms are re-taught from scratch as the course needs them.

A laptop with Ubuntu (or WSL2 / Docker on Windows). No robot, no depth camera and no GPU required — everything runs in simulation and on free datasets.

Description

This course contains the use of artificial intelligence.Every autonomous robot has to answer two questions at the same time:Where am I, and what does the world around me look like?Visual SLAM, or Simultaneous Localization and Mapping using cameras, is how robots solve both. It is used in drones, warehouse robots, AR headsets, autonomous vehicles, and many other systems that need to understand motion and build maps without relying entirely on GPS.This course takes Visual SLAM apart and builds it back up from first principles.Build Visual SLAM from scratchYou will create a working SLAM pipeline in Python, one component at a time.You will start with:Camera calibrationORB feature detection and matchingEssential matrix estimationRANSACRelative camera motion3D point triangulationPnP pose estimationVisual odometryYou will watch your own camera trajectory appear on screen from the system you built.Turn visual odometry into SLAMNext, you will add the components that make it a complete mapping system:Keyframes and map pointsLocal and global bundle adjustmentPose graph optimizationBag-of-words place recognitionLoop closureTracking failure detectionRelocalizationYou will also understand one of monocular SLAM's biggest limitations: scale ambiguity, and learn how stereo and RGB-D cameras solve it.Add IMUs and ROS 2You will then move from pure visual SLAM into robotics applications:Stream camera data through ROS 2Fuse camera and IMU measurementsUnderstand visual-inertial odometryCompare your implementation with ORB-SLAM3 and RTAB-MapConnect SLAM outputs to a robot navigation stackBenchmark like a robotics researcherYou will evaluate SLAM performance using public datasets including:KITTIEuRoCTUM RGB-DInstead of deciding whether a trajectory "looks good," you will measure localization and trajectory error using the same ideas commonly used in robotics research.No expensive hardware requiredEverything can be completed using:A normal laptopSimulationFree public datasetsPython and ROS 2No robot, depth camera, or GPU is required.By the end of the course, you will not just know what Visual SLAM is. You will have built one yourself.You will understand front ends, back ends, keyframes, bundle adjustment, loop closure, visual-inertial odometry, and the mathematical ideas underneath them.Most importantly, you will be able to look at a drifting trajectory or broken map and reason about why it failed and how to debug it.

Robotics engineers and students who can use SLAM as a black box but want to understand and modify what happens inside it.,Computer vision developers moving into robotics who want to apply feature matching and multi-view geometry to a real navigation stack.,ROS 2 developers who need reliable localization and mapping from cameras instead of expensive 3D LiDAR.