Juhyung's Projects

github:   https://github.com/Juhyung-L/visual_odometry Visual odometry is estimating the change in position of a robot using the image frames from a moving camera. It works by taking individual frames from a prerecorded video or live feed of a camera and putting it through a processing pipeline. The first step in the visual odometry pipeline is feature detection. A feature in an image is a point in the image that can be easily identified in multiple views of the same scene.  These two images are pictures of the same building taken from two different view points. There are parts of the building that can easily be identified in both pictures. For example, the three arches on the bottom floor or the circular pattern at the center of the building. For the human brain, features are usually encoded as whole structures just as I have listed above, but they are points for computers and they are identified using mathematical techniques involving the pixel color values. There are a variety of di

github:  https://github.com/Juhyung-L/cuda_icp Background Scan matching algorithms are ubiquitous in the field of mobile robotics. Although there are countless variations of the algorithm with each one having its benefits and drawbacks, the overarching goal of the algorithms are the same. Given two sets of points, the algorithm finds the optimal rigid body transformation (rotation and translation) that results in the least error. They are specifically useful in localization algorithms as finding the rigid transformation between two consecutive laser scans from the same robot means finding the accurate change in position and orientation of the robot. One of the most commonly used variation of the scan matching algorithm is the iterative closest point (ICP) algorithm.  ICP takes a 2-step approach to scan matching Finding the corresponding pairs of points in the two point sets Finding the rigid body transformation Finding corresponding pairs means finding two points (one in each point set