What is SLAM AR?

SLAM (Simultaneous Localization and Mapping) is a technology which understands the physical world through feature points. This makes it possible for AR applications to Recognize 3D Objects & Scenes, as well as to Instantly Track the world, and to overlay digital interactive augmentations.

What is IMU odometry?

Two of the simplest ways to generate odometry is to use IMU (inertial measurement unit) and the GPS. GPS provides the device with the global position, and is often used as the ultimate calibration data against all the sensors. And with the GPS position data over time, it can likewise be used to generate odometry.

How does bundle adjustment work?

Bundle adjustment boils down to minimizing the reprojection error between the image locations of observed and predicted image points, which is expressed as the sum of squares of a large number of nonlinear, real-valued functions. Thus, the minimization is achieved using nonlinear least-squares algorithms.

What is visual SLAM and how does it work?

On the contrary, Visual SLAM maintains a map over the entire camera trajectory, to be able to perform loop closure detection and correction which can improve drift a lot when the camera keeps revisiting certain parts of the environment.

What is the difference between Slam and odometry?

First, we have to distinguish between SLAM and odometry. Odometry is a part of SLAM problem. It estimates the agent/robot trajectory incrementally, step after step, measurement after measurement. The next state is the current state plus the incremental change in motion.

Is visual odometry dead-reckoning?

Visual Odometry isn’t practically real ‘odometry/dead-reckoning’ which would be frame-to-frame tracking, and prone to severe drift. Typically, visual odometry pipelines also maintain a map [0] (e.g. in the form of a few past keyframes), albeit over a short time window.