ORB-SLAM in Summary

ORB-SLAM in Summary

    ORB-SLAM, which stands for "Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping," is a computer vision and robotics technique used for simultaneous localization and mapping (SLAM). SLAM is a crucial technology in the field of robotics and computer vision, which allows a robot or a device to map its environment and determine its own position within that environment in real-time.

    ORB-SLAM was developed as an open-source visual SLAM system and has been widely used in robotics, autonomous vehicles, augmented reality, and other applications. It relies on a combination of key features, including the ORB (Oriented FAST and Rotated BRIEF) feature detector and descriptor, as well as other computer vision techniques like FAST (Features from Accelerated Segment Test) and BRIEF (Binary Robust Independent Elementary Features).

    Here's how ORB-SLAM typically works:

    Feature Detection: ORB-SLAM identifies distinctive features in the environment, such as corners or key points in images or video frames.

    Feature Matching: It tracks these features across multiple frames, allowing the system to establish correspondences between features in different frames.

    Pose Estimation: Using these correspondences, ORB-SLAM estimates the camera's pose (position and orientation) in relation to the mapped environment.

    Map Building: Simultaneously, it builds a map of the environment based on the identified features and their positions in 3D space.

    Loop Closure: ORB-SLAM incorporates techniques for detecting and closing loops, which help correct any accumulated errors in the estimated trajectory and map.

    Real-time Operation: ORB-SLAM is designed to operate in real-time, which makes it suitable for applications where robots or devices need to navigate and interact with their environment as they move.

    Overall, ORB-SLAM is a powerful tool for enabling robots and devices to navigate and understand their surroundings, making it useful in a wide range of applications, from autonomous drones to augmented reality systems. It's important to note that ORB-SLAM is just one of many SLAM algorithms, and the choice of which SLAM system to use depends on the specific requirements and constraints of a given application.


    The FAST (Features from Accelerated Segment Test) algorithm is a corner detection method used in computer vision, and it's typically defined through a mathematical representation. The algorithm operates by comparing the brightness of pixels in a circular neighborhood around a candidate pixel to determine if the candidate pixel is a corner or key point.

    The mathematical representation of the FAST algorithm involves the following steps:

    Intensity Test: Consider a candidate pixel "C" in an image. The algorithm compares the intensity of this pixel to the intensities of a set of surrounding pixels arranged in a circle.

    Threshold: The algorithm uses a threshold value "t." It checks whether a sufficient number (usually 12 or more) of the surrounding pixels are brighter or darker than the candidate pixel "C" by at least the threshold value "t."

    Corner Decision: If enough pixels in the circle satisfy the intensity comparison condition, the candidate pixel "C" is classified as a corner or key point. Otherwise, it's considered a non-corner pixel.

    Here is a simplified mathematical representation of the FAST algorithm:

    1. Let I(x, y) represent the intensity of the candidate pixel "C" at coordinates (x, y).
    2. Let I_p represent the intensity of a surrounding pixel located on the perimeter of the circle with radius 3 pixels.
    3. Let t be the threshold value.
    4. FAST defines a set of at least 12 surrounding pixels as "corner pixels" if:

    The above is the basic math, how FAST works