Autonomous racing with the Anki Overdrive

Overview

Anki Autonomous Racing

Autonomous racing with the Anki Overdrive. Using the Overdrive-Python API (https://github.com/xerodotc/overdrive-python) develop sensing, state estimation, and control techniques for static obstacle avoidance using the Anki Overdrive vehicle.

Running the code: Run "roslaunch anki_drive anki_cv.launch" to launch the USB camera tracking. Following this, you may run "rosru anki_drive planning.py" to run the obstacle avoidance routine for the Anki Overdrive. Within this file, in the "main" method, you may change the type of controller you'd like by setting its parameter to "True." For example, if you'd like to use MPC for obstacle avoidance, you may change the variable do_MPC to True, and change all other controller variables (do_P, do_PD, do_PD_setpoint) to False. The car will then use MPC to determine its path.

System ID: If you wish to reperform system ID of the Anki Overdrive vehicle, where a model later used by MPC is fit to the vehicle, uncomment "trial_SysID.sysID_datacollect(pointFromPixel, last_image_service)" in the main method of mapping_track_to_model.py. This will enable and run the data collection, storage, and regression procedures. After uncommenting, run "rosrun anki_drive mapping_track_to_model.py" to perform the system ID.

Personalized Federated Learning using Pytorch (pFedMe)

Personalized Federated Learning with Moreau Envelopes (NeurIPS 2020) This repository implements all experiments in the paper Personalized Federated Le

Charlie Dinh 226 Dec 30, 2022
Generate Cartoon Images using Generative Adversarial Network

AvatarGAN ✨ Generate Cartoon Images using DC-GAN Deep Convolutional GAN is a generative adversarial network architecture. It uses a couple of guidelin

Aakash Jhawar 50 Dec 29, 2022
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning Authors: Tao Yu* Yichi Zhang* Zhiru Zhang Christopher De Sa *: Equal Contri

Cornell RelaxML 4 Sep 08, 2022
Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation Efficient Self-Ensemble Framework for Semantic Segmentation by Walid Bousselham

61 Dec 26, 2022
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

HiddenLayer A lightweight library for neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. HiddenLayer is simple, easy to ex

Waleed 1.7k Dec 31, 2022
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The unofficial code of CDistNet. Now, we ha

25 Jul 20, 2022
Datasets and pretrained Models for StyleGAN3 ...

Datasets and pretrained Models for StyleGAN3 ... Dear arfiticial friend, this is a collection of artistic datasets and models that we have put togethe

lucid layers 34 Oct 06, 2022
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

111 Dec 29, 2022
Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation: Work In Progress, Results can't be replicated yet with the m

Yad Konrad 196 Aug 30, 2022
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 06, 2022
A Pytree Module system for Deep Learning in JAX

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

Cristian Garcia 216 Dec 20, 2022
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

37 Dec 28, 2022
The authors' official PyTorch SigWGAN implementation

The authors' official PyTorch SigWGAN implementation This repository is the official implementation of [Sig-Wasserstein GANs for Time Series Generatio

9 Jun 16, 2022
(NeurIPS 2020) Wasserstein Distances for Stereo Disparity Estimation

Wasserstein Distances for Stereo Disparity Estimation Accepted in NeurIPS 2020 as Spotlight. [Project Page] Wasserstein Distances for Stereo Disparity

Divyansh Garg 92 Dec 12, 2022
A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks without the use of any outside machine learning libraries - all from scratch.

Kordel K. France 2 Nov 14, 2022
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
Realtime YOLO Monster Detection With Non Maximum Supression

Realtime-YOLO-Monster-Detection-With-Non-Maximum-Supression Table of Contents In

5 Oct 07, 2022
Implementation of Nalbach et al. 2017 paper.

Deep Shading Convolutional Neural Networks for Screen-Space Shading Our project is based on Nalbach et al. 2017 paper. In this project, a set of buffe

Marcel Santana 17 Sep 08, 2022