SLAM Learning Resources
This post organizes the resources that I used to learn SLAM, also serves as a reference to other post in this blog.
This post organizes the resources that I used to learn SLAM, also serves as a reference to other post in this blog.
This post is the notes of book “State Estimation for Robotics”, which talks about the state estimation math for SLAM.
This post is the notes of Cyrill Stachniss’s Photogrammetric Computer Vision course. This post includes concepts about Visual Features, RANSAC, Extrinsics and Intrinsics, Fundamental and Essential matrices and so on.
Notes of Book Multiple View Geometry in Computer Vision
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No folding
title
With folding
Anchor: a kind of data structure in AR field, which represents a precise point of interest in 3D space that can be shared across users, devices, and time
Microsoft Azure Spatial Anchors is a service that can store the local world anchors to the cloud and share the anchors among different platforms, see the table blow. ASA (Azure Spatial Anchors) enables building apps that can map, persist, and share 3D content from room-scale to real world-scale.
Noise exists in the world, ans also exists in the robot motion and observations, so robots never know exactaly of the environment and themselves. However, probality theory could represent the uncertainty and give a better guess or estimation of the world. Therefore, the state estimation is very important in Robotics.
ARKit is a software framework for iOS to create AR applications. It support features of device tracking, plane detection, and could allow apps to place virtual objects on top of real-world environment.
Introduce VR, AR.
There are a lot of application development paths for Microsoft MixedReality platforms. This post covers things including OpenXR, Unity, Stereo Kit, MRTK and etc.