Core components of visual SLAM

WHAT THIS TESTS The question checks conceptual understanding of how a moving camera localizes itself while building a map at the same time, and why drift makes loop closure necessary. Strong answers separate the responsibilities cleanly.

A GOOD ANSWER COVERS Tracking is the front end: it extracts and matches features between consecutive frames, estimates the camera pose for each frame typically by minimizing reprojection error, and runs fast enough for real time. Mapping is the back end: it triangulates new 3D points, maintains keyframes, and refines poses and landmarks together using local bundle adjustment to keep the map accurate. Loop closure detects when the camera revisits a known location, usually via appearance-based place recognition like bag-of-words, then performs pose-graph or global bundle adjustment to redistribute accumulated drift, making the trajectory globally consistent.

COMMON WRONG ANSWERS Saying tracking builds the map, conflating the front and back ends. Or omitting loop closure, which leaves the map drifting unboundedly over long runs. Or describing it as pure odometry with no global optimization.

LIKELY FOLLOW-UPS What is the difference between SLAM and visual odometry, the answer being loop closure and a globally consistent map. How does bundle adjustment work. Why is drift inevitable without loop closure. How do you detect loops robustly and avoid false positives.

ONE CONCRETE EXAMPLE A robot drives a loop around a room. Tracking estimates each frame's pose in real time and mapping builds a sparse point cloud of landmarks, but small per-frame errors accumulate as drift, so the start and end of the trajectory do not align. When the robot returns to the entrance, loop closure recognizes the scene through appearance-based place recognition, adds a constraint linking the current pose to the earlier one, and runs global pose-graph optimization that snaps the trajectory closed and redistributes the error, correcting the whole map so it is globally consistent.