makarandmandolkar Goto Github PK

a-loam

Advanced implementation of LOAM

agribot

The mission of the project is to build an agricultural robot (AgriBot) from scratch with the aim of serving as a data-recording platform in fields. For further information about the design and purpose of the robot, please follow the About the AgriBot Project page

apollo

An open autonomous driving platform

application-of-logistic-regression

Logistic Regression expands on linear regression as a classification algorithm which has only two possible outcomes, which are known as classes. It is a classification model that uses input variables to predict a categorical outcome variable that can take on one of a limited set of class values. Logistic regression uses the sigmoid function to weighted input values to generate a prediction of the data class.

astar-implementation_cpp

autonomous-global-planning-with-suitable-shortest-path-algorithm-and-local-planning-for-navigation

The objective is to autonomously plan and execute a path for a robot in the Stage simulator from a start location to a goal location, given a map. The global plan is given by A* and the local planning is done using a modified Vector Field Histogram (VFH).

batvh_update_robot-localization-ekf

simulation on SLAM using Extended Kalman filter

canopennode

CANopen stack base.

carla-brain

Autonomous Car System Integration Project on Udacity SDC ND

carnd-semantic-segmentation

core_perception

For combining point cloud (https://answers.ros.org/question/346156/merging-point-clouds/)

cpp-nanodegree

Udacity C++ nanodegree

cppnd-capstone-snake-game

A 2D Snake game using C++ and SDL

cppnd-memory-management-chatbot

cppnd-program-a-concurrent-traffic-simulation

The final project for the Udacity C++ Nanodegree Concurrency course

cppnd-route-planning-project

cppnd-system-monitor-project-updated

System Monitor Project from the Object Oriented Programming Course of the Udacity C++ Nanodegree Program

devika

Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

ekf-localisation

Estimating the 2D pose of a robot using sensor measurements by applying an Extended Kalman Filter (EKF).

ekf-localization

Estimating the 2D pose of a robot using sensor measurements by applying an Extended Kalman Filter (EKF).

ekf-slam-on-turtlebot3

Feature-based EKF-SLAM on Turtlebot3 with landmark detection

ekf_localization_known_correspondences

Implementation based on Probabilstic Robotics by Thrun et. al.

ekf_slam

Implements Extended Kalman Filter based Localization and Mapping on the Victoria Park Dataset

eventemitter2

A nodejs event emitter implementation with namespaces, wildcards, TTL, works in the browser

extended-kalman-filter

Compare the Position of a Turtlebot simulated in a Gazebo environment with a Filtered(Extended Kalman Filter) and a Unfilered Trajectories

extended_kalman_filter

Extended Kalman filter for estimating the position of a vehicle along a trajectory using available measurements and a motion model

extended_kalman_filter_python

Python implementation of an Extended Kalman Filter.

grid-localization-and-state-estimation-using-bayes-filter

The objective is to get fimiliear with grid localization, which is a discrete variant of Bayes localization.

icp_based_no_landmarks_ekf_localization

A ROS package for mobile robot localization using an extended Kalman Filter

implementation-and-analysis-of-unsupervised-learning

In this project we have made use of the Fashion-MNIST clothing classification problem dataset forouranalysis,whichisacomparativelyanewandstandarddataset being used in machine learning problems. In this project we have studied the implementation and analysis of the methodology of K-Means Clustering, Auto Encoder and Decoder, and Gaussian Mixture Model (GMM). Uponimplementation, we were able to achieve an accuracy of 51.29% in K-Means, in K-Means with autoencoder the accuracy achieved is 56.54% and 59.29% for Gaussian Mixture Model using auto-encoder models