binhu85 Goto Github PK

acc2021

asif

CBF library used in a wide variety of simulation and hardware implementations. Embedded friendly, requires OSQP. Library originally written by Thomas Gurriet. Contains slightly modified version of libaffa, found here: https://github.com/ogay/libaffa

awesome-neural-ode

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

binhu85.github.io

cerlab-uav-autonomy

[CMU] A Versatile and Modular Framework Designed for Autonomous Unmanned Aerial Vehicles [UAVs] (C++/ROS/PX4)

darts

Differentiable architecture search for convolutional and recurrent networks

deeprl

Modularized Implementation of Deep RL Algorithms in PyTorch

diffusion_policy

[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion

faster

3D Trajectory Planner in Unknown Environments

gcopter

A General-Purpose Trajectory Optimizer for Multicopters

kimera-multi

Index repo for Kimera-Multi system

lio-sam

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

lvi-sam

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

mav_trajectory_generation

Polynomial trajectory generation and optimization, especially for rotary-wing MAVs.

ml-zoo

onboard_detector

[IEEE RA-L'23] Dynamic Obstacle Detection and Tracking (DODT) algorithm for Autonomous Robots (C++/ROS)

osqp

The Operator Splitting QP Solver

pgm-spring-2019

Course webpage for PGM, Spring 2019.

quadrotor

Quadrotor control, path planning and trajectory optimization

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

slam_in_autonomous_driving

《自动驾驶中的SLAM技术》对应开源代码

tiny-training

On-Device Training Under 256KB Memory [NeurIPS'22]

tog

Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG,