State-of-the-art AI Models for processing Lidar data in order to detect objects: Pointnet, Voxelnet, SECOND, and Pointpillar are explained — A major breakthrough in recognition and detection tasks on images was due to moving from hand-crafted features to machine-learned features. PointNet the first, an end-to-end deep neural network that learns point-wise features directly from point clouds brought the same breakthrough for point cloud data and after this new architecture, various…

Intermediate git commands and their meanings explained — I have been using GIT for versioning my work repositories as many programmers and I wanted to make a review for the most used commands and their logic to have a deeper understanding. Here is a collection that I have made after some online tutorials and classes that I would…

Introduction to Lidar, Autonomous Driving Public Dataset Types, State of the Art Object Recognition Models for Lidar, and a Kalman Filter-based Tracking Algorithm is explained in this tutorial — Hello everyone! I have completed my master's program after a very busy last 6-months period where I was having my internship and preparing my master's thesis report. Without surprise, my project was about “3D Multi-Object Tracking using Lidar for Autonomous Driving” and as a very beginner in Lidar, Tracking, and…

A comprehensive guide to convolution and convolutional neural networks for image classification, from implementation with Python and TensorFlow to optimization and transfer learning techniques — We learned Neural Network Fundamentals in my previous post Neural Networks Fundamentals A quick intro for Neural Networks, Unsupervised and Supervised Learning, the difference between Regression and…towardsdatascience.com

Epipolar Geometry basics like Essential Matrix, Fundamental Matrix, Triangulation, Feature Matching are explained with their MATLAB code implementations — We learned how to use a projective camera and how to calibrate it. In this post, we will learn Epipolar Geometry, which is the intrinsic projective geometry between two views. Two views mean now we will learn how to work with more than 1 camera to perform different tasks!

Camera Calibration using DLT and Zhang’s Method from theoretical math explanation to MATLAB code application — We learned what Camera Calibration Matrix is in the previous post of this series. A general issue in Visual Perception is that usually, we need to calibrate our cameras before using them. That means, we need to estimate Camera Calibration Matrix parameters (intrinsic parameters) and extrinsic parameters (rotation and translation…

A user interface implemented with QT Creator, Opencv, and C++ to make the user able for realizing what they learned during this tutorial like Camera Calibration, Fundamental-Essential Matrix Estimation, Triangulation — The GUI consists of 3 parts as Calibration, Feature Matching, and Epipolar Geometry. Let’s examine it part by part Calibration In this part, you can calibrate your camera either using your already taken images or using your camera connected to your PC. Additionally, there is a Demo section for you…

A series about Visual Perception Fundamentals like Camera Calibration, Epipolar Geometry, and their mathematical implementations from scratch — Visual perception is the human ability to interpret the surrounding environment. [1] In this series, we will learn the Visual Perception fundamentals for robotics without using any Artificial Visual Perception method but using classical approaches step by step.

A brief explanation of these 3 terms that form the basis of relation— similarity analysis for different subjects like Region Growing, an algorithm used in Image Segmentation — Neighborhood The neighborhood of a pixel is simply the pixels that touch it. We examine neighborhood in 3 categories: 4- Neighborhood Here, we talk about the pixels touching in 4 directions top, right, bottom, and left. d- Neighborhood