JulianKu
Researcher at LUH, research on autonomous UAVs for deployment in disaster scenarios, M.Sc. Robotics, Cognition, Intelligence from TUM
Leibniz University HannoverHannover, Germany
Pinned Repositories
3d-moments
Code for CVPR 2022 paper '3D Moments from Near-Duplicate Photos'
House-Price-Prediction
Intelligent Decision Support System for House Price Prediction
k-KISSME
kernelized large-scale metric learning from equivalence constraints
LeGO-LOAM
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
megastep
megastep helps you build 1-million FPS reinforcement learning environments on a single GPU
MengeUtils
Collection of utilities for working with Menge configuration and output files
MiningMassiveDatasets
Course Project on Large-Scale ML
Pedestrian-Detection-and-Tracking
Integration of YOLO Object Detection and SORT Tracking algorithm applied to JAAD dataset
potential_fields_based_obstacle_avoidance
This code can be used on a mobile robot for avoiding obstacles. This code was originally written for use with a Summit XL mobile robot from Robotnik using laser readings as the means for determining obstacles. However it can be configured to be used for any mobile robot using either laser and/or sonar readings. It is based on the use of a potential
JulianKu's Repositories
JulianKu/Pedestrian-Detection-and-Tracking
Integration of YOLO Object Detection and SORT Tracking algorithm applied to JAAD dataset
JulianKu/House-Price-Prediction
Intelligent Decision Support System for House Price Prediction
JulianKu/k-KISSME
kernelized large-scale metric learning from equivalence constraints
JulianKu/3d-moments
Code for CVPR 2022 paper '3D Moments from Near-Duplicate Photos'
JulianKu/LeGO-LOAM
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
JulianKu/LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
JulianKu/megastep
megastep helps you build 1-million FPS reinforcement learning environments on a single GPU
JulianKu/MengeUtils
Collection of utilities for working with Menge configuration and output files
JulianKu/MiningMassiveDatasets
Course Project on Large-Scale ML
JulianKu/potential_fields_based_obstacle_avoidance
This code can be used on a mobile robot for avoiding obstacles. This code was originally written for use with a Summit XL mobile robot from Robotnik using laser readings as the means for determining obstacles. However it can be configured to be used for any mobile robot using either laser and/or sonar readings. It is based on the use of a potential
JulianKu/Python-RVO2
Optimal Reciprocal Collision Avoidance, Python bindings
JulianKu/torchrl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.