bryan-rathos Goto Github PK
Name: Bryan Rathos
Type: User
Company: DEKA Research and Development Corp.
Bio: MS Robotics Engineering from WPI. Computer Vision | Sensor Fusion | Deep Learning.
Location: Manchester, NH
Name: Bryan Rathos
Type: User
Company: DEKA Research and Development Corp.
Bio: MS Robotics Engineering from WPI. Computer Vision | Sensor Fusion | Deep Learning.
Location: Manchester, NH
• Built and trained a CNN for steering angle prediction of an autonomous vehicle using end-to-end learning to mimic human driving behavior. Model was build using Keras framework and trained with RGB images on AWS EC2. • Performed data augmentation, pre-processing, and subsampling to make the model robust on unseen track data.
Particle filter implementation for vehicle localization
Tools for Sensor Fusion processing.
CarND Term 2 Model Predictive Control (MPC) Project
CarND MPC Quizzes
Create a path planner that is able to navigate a car safely around a virtual highway
Self-Driving Car Nanodegree Program Starter Code for the Unscented Kalman Filter Project
Coding interview questions
:blue_book: A tour about C++ with source code examples
Public facing notes page
Self-Driving Car Nanodegree Program Extended Kalman Filter Project
Fast Model Predictive Control using the Primal Barrier Method
Learn OpenCV : C++ and Python Examples
Stereo and mono visual odometry Open folder mono for 2D-2D monocular visual odometry implementation and folder stereo for 3d-2d stereo visual odometry implementation
Using Mxnet!!!!
Supporting code for short YouTube series Neural Networks Demystified.
A repository for a presentation on debugging and performances tricks with MXNet Gluon
Python sample codes for robotics algorithms.
Simple examples to introduce PyTorch
A self-driving car simulator built with Unity
Mid-way progress of the 3D tracking project. This repo contains exhaustive comparisons of the detector+descriptor combinations with respect to number of features detected and time taken for execution of the combination.
Tracking 3D bounding boxes and computing Time To Collision based on LIDAR and camera.
• Developed point cloud object detection pipeline and implemented RANSAC for segmentation, KD-Tree and Euclidean clustering algorithms from scratch. • Verified results of segmentation and clustering with PCL functions and used PCL visualizer for displaying the results for both the methods.
Implementation of Radar target generation and detection using 2D FFT to form Range Doppler Map and 2D CA-CFAR technique for noise suppression.
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
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Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.