#Visual-SLAM

Computer Vision Primer September 7, 2026

Visual-SLAM Primer — How a Camera Learns "Where Am I"

With no GPS and no map, a robot can still find its own position and build a map of its surroundings from camera images alone. From feature tracking through epipolar geometry, the difference between Visual Odometry and SLAM, the lineage of landmark algorithms from ORB-SLAM to LSD-SLAM and DSO, and Bundle Adjustment, a systematic ground-up tour of how Visual-SLAM actually works.

Fundamentals · 18 min Computer Vision Primer September 3, 2026

Epipolar Geometry — Reading Depth and Camera Motion from Two Images

Epipolar geometry is the theory that constrains how the same 3D point projects into two images. This article systematically explains the Fundamental Matrix and Essential Matrix, the 8-point and 5-point algorithms, RANSAC, triangulation, the choice between epipolar geometry and homography, calibration, OpenCV/COLMAP implementation, failure conditions, and recent learning-based estimation, with equations and diagrams.

Fundamentals · 16 min Technology Trends July 27, 2026

Technology Trends in Visual-SLAM

From classical feature-point methods to deep-learning-based end-to-end estimation, and on to a reinvention of map representation via 3D Gaussian Splatting/NeRF. Surveying where Visual-SLAM — self-localization and mapping from cameras alone — stands today.

Trends · 6 min