Implementation of SLAM on a 2D Graph from Scratch using Python

What is SLAM?

Simultaneous localization and mapping (SLAM) used in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. In other words, SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. In addition to localization, we also want to build up a model of the robot’s environment so that we have an idea of objects, and landmarks that surround it and so that we can use…


A Stepwise Implementation using Python

What are Kalman Filters?

Kalman filter finds the most optimum averaging factor for each consequent state. Also somehow remembers a little bit about the past states. It performs a joint probability distribution over the variables for each timeframe. The algorithm uses the new mean and new variance for every step in order to calculate the uncertainty and tries to provide accurate measurements for each timeframe of the measurement update (sensing/prediction) and the motion update(moving). The algorithm also uses other inaccuracies and statistical noise in order to represent the initial uncertainty.

Purpose of Kalman Filters:

  • Transform data input from various sensors like LiDAR and Radar trackers into a usable…


A Pytorch implementation of the CNN+RNN architecture on the MS-COCO dataset.

Generally, a captioning model is a combination of two separate architecture that is CNN (Convolutional Neural Networks)& RNN (Recurrent Neural Networks) and in this case, LSTM (Long Short Term Memory), which is a special kind of RNN that includes a memory cell, in order to maintain the information for a longer period of time. …


Behavioral Uncertainty in Reinforcement Learning [ ICAART 2014 / Paper Summary]

In this paper, the authors tackle the drawback of uncertainty that an agent has to face in order to survive and make proper choices in an environment that classical probability wouldn’t be able to represent properly. so, the authors suggest the use of quantum probability instead.

Operant conditioning is a process of learning which practices rewards & punishments strategy for a particular behavior(response). With operant conditioning, a relationship between behavior and consequences (outcomes) for that particular behavior comes into the picture. It is a derivative of associative learning which is used in modeling reinforcement learning. However, modeling operant conditioning and…


I came across this project on Udacity’s Computer Vision Nanodegree Program, Thanks to Udacity and Facebook.ai for awarding me with the Nanodegree Scholarship Program. Since I have successfully completed this project; I’m going to share some insights regarding the project and as well as the other projects in the program in later articles as I progress through the program.

In this article, I’m going to write about how to build a facial keypoint detection model using CNN.

What are Facial Key-points?

Facial Keypoints are also called Facial Landmarks which generally specify the areas of the nose, eyes, mouth, etc on the face, classified by…


Overview:

This script filters out the NSFW posts and scrapes only the Non-NSFW posts which are stored in saved posts from your Reddit account.

As I was implementing this script, I realized that my code isn’t universal. Therefore, I would like to provide you with my code and in order to get the script running for you, you need to tweak some class names or element id according to your Reddit account via CSS query selector by inspecting elements in your browser.

However, Do run the script initially by inserting your credentials wherever asked. for example wherever there is “Your-username”…

Krunal Kshirsagar

Research Engineer & Writer @OpenMinedorg | LinkedIn: https://www.linkedin.com/in/krunal-kshirsagar | Github: https://www.github.com/Noob-can-Compile

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