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How Robots Make Maps— an Intro to SLAM (Simultaneous Localisation and Mapping)

A big picture of odometry, localisation, mapping, feature matching and loop closure for non-roboticists

Photo by Dominik Scythe on Unsplash

Perfection or efficiency? — SLAM, SfM or VO

Why do we have two eyes? — Stereo, Monocular, or Depth Camera

How do we find correspondences and consistency between frames? — Feature-based Method vs Direct Method

How do we adjust camera poses? — RANSAC, Iterative Closest Point

How do we correct the map? — Filter Methods vs Bundle Adjustment

(a) Bundle Adjustment over all camera poses and features (b) Filter method (c) Key frame Bundle Adjustment. Image taken from [12]. T: camera trajectory, x: features

When do we close the loop? — Place Recognition

Challenges in SLAM

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Shu Ishida

DPhil student at University of Oxford, researching in computer vision and deep learning. Enjoys programming, listening to podcasts, and watching musicals.