Here are some great explanatory posts from Udacity Self-Driving Car students about Kalman filters, computer vision, and how to build a mini autonomous vehicle.
Vivek has transposed some of the notes from his Advanced Controls course at SUNY-Stony Brook. These notes are great for understanding the intuition of how Kalman filters reduce uncertainty:
“This process of combining system dynamics with state measurements is the underlying principle of Kalman filters. Kalman filters provide good estimation properties and are optimal in the special case when the process and measurement follow a Gaussian distributions.”
Milutin provides a clear and thorough explanation of his pipeline for detecting vehicles using HOG and Linear SVM:
“The goals/steps of this project are the following:
Extract the features used for classification
Build and train the classifier
Slide the window and identify car on an image
Filter out the false positives
Calculate the distance
Run the pipeline on the video”
I like the intuitive explanation that Arnaldo provides for the histogram of oriented gradients (HOG) algorithm:
“The HOG extractor is the heart of the method described here. It is a way to extract meaningful features of a image. It captures the “general aspect” of cars, not the “specific details” of it. It is the same as we, humans, do: in a first glance, we locate the car, not the make, the plate, the wheel, or other small detail.
HOG stands for “Histogram of Oriented Gradients”. Basically, it divides an image in several pieces. For each piece, it calculates the gradient of variation in a given number of orientations.”
I love the mini autonomous vehicle that Shrek built, and especially I love that he trained a deep neural network so that the car react to traffic signs!
“driver.py — is the heart of the project. It includes the image processing pipeline that identifies the traffic sign from the camera by using the previously trained DNN and then send appropriate driving control signals to the car.”
Alena went beyond the minimum requirements for the Advanced Lane Finding Project, and she got her computer vision pipeline to work on the challenge video:
“When the first line was identified successfully, I use line equation with margin as an area to search for the next line. No need to do Sliding Window Search again.”