Eugeniu
Vezeteu

Autonomous systems Robotics & AI

Who am I?

Problem solver first, engineer second

CV

MSc student of EIT Digital (double degree studies), ICT Innovation - Robotics & Artificial Intelligence. Primary research interests - data science, perception & control in robotics. Strong passion for AI and machine learning.

 

About me

I am a computer scientist with a robotics background.

In 2019 I became a master student member EIT Digital, studying Autonomous systems - Robotics and Machine learning. I did the first master year (2019-2020) at Technical Universit of Berlin, with 3.35 / 4.00 GPA. On July 2021 I finishied my master at Aalto University, School of Electrical Engineering, in Autonomous systems Robotics & AI. Since January 2021 I have been working as a researcher at the Finnish Geospatial Research Institute (FGI) under the National Land Survey of Finland (NLS).

In August 2018 - September 2019 I worked at Multisoft Constanta as Full-stack software engineer. There I developed skills in analysis, design and implementation of web and desktop applications using C#, .NET, SQL Server, Javascript (Bootstrap & jQuery), provide cloud solutions for hotels and restaurants.

In 2017-2019 I obtained a master's degree in "Distributed Multi-Model Virtual Environments" at Ovidius University of Constanta with final thesis grade: 9.66 / 10 and overall average grade: 9.78 / 10
In 2015-2018 I did my bachelor in Mathematics and Computer Science, at Ovidius University of Constanta. I graduated in Mathematics and Computer science with final thesis grade: 10 / 10 and overall average grade: 9.87 / 10.
In 2014 I did my first bachelor's degree in economics and business administration, at Ovidius University of Constanta. I graduated in 2017 with final bachelor thesis grade: 10/10 and overall average grade: 8.57 / 10.

Awards and Honors

  1. 1st prize ESTIC 2018 - Speech and Speaker recognition app for Romanian language, EDITION XI, Contest for Students in Information Technology.Link

  2. 1st prize ESTIC 2019 - Mobile robot embedded with object detection and recognition app, EDITION XII, Contest for Students in Information Technology, Link

  3. UNIVERSITTY OVIDIUS OF CONSTANTA, SUMMER SCHOOL, July 2017 | Constanta, Romania
    Cerva Summer school on virtual environments, cultural heritage 6’th edition. Link

  4. KTH ROYAL INSTITUTE OF TECHNOLOGY, SUMMER SCHOOL August 2020 | Stockholm, Sweden
    BIG DATA ANALYTICS - EIT Digital summer school, Link

THESIS & PUBLICATIONS

  1. Speech & Speaker recognition for Romanian Language, Published in RoCHI 2018, Computer Science, Romania.

Recommendations

  1. PhD. Associate Professor of Computer Science DRAGOS SBURLAN, Constanta, 08/01/2019

  2. Conf.univ.dr. ELENA PELICAN, Constanta, 25/11/2018

Master following topics

  • Object-Oriented Programming, Dynamic programming
  • Relational databases, Front-end, Back-end development
  • Machine learning
    • Unsupervised
      • PCA, Outlier Detection, LLE
      • Clustering, K-means, GMM, Expectation-Maximization, Hierarchical clustering
    • Supervised
      • Kernel Ridge Regression, Cross-Validation, Logistic regession
      • Support vector machines, Boosting
      • Neural networks, CNN, LSTM
      • Feature learning, selection
      • Preference learning, ranking
    • Reinforcement learning
      • Value Iteration, Policy Iteration
      • Q-Learning, DQN
      • Policy Gradient, Actor critic
  • Computer vision
    • Image formation and processing
    • Feature detection and matching
    • Structure from motion, stereo and 3D reconstruction
    • Object recognition and detection
  • Robotics
    • Forward and Inverse Kinematics
    • Path planning & tracking
    • Filtering, localization, tracking, mapping
    • Kalman Filter, EKF, UKF, Particle filter
    • ROS, SLAM, Navigation
    • Image-based, Position-based visual servoing

SKILLS

  • PROGRAMMING
    • Good with
      • Python: (Numpy, Scipy, OpenCV, Pytorch, Keras, Flask)
    • Known programming languages:
      • C# •C++ •javascript •SQL • PL / SQL • JAVA • CSS • HTML5
    • Basics of:
      • 3D modelling: •Blender •3DS max
    • Experienced with:
      • Panda 7dof arm •holonomic,nonholonomic car • Linux • Git
      • Lidar • IMU • SLAM • ROS • nvidia jetson nano •raspberry pi •Arduino
  • COMPETENCES
    • English language C1
    • Russian language C2
    • Romanian - native
    • Driving license AM, B1, B

Robotics & AI projects

3D LiDAR pose-graph slam
Source Code

  • Estimate the transformation between laser scans with ICP;
  • Construct the keyframe pose-graph in the FrontEnd;
  • Optimize the graph in the BackEnd;

Python implementation of the RGB-D SLAM
Source Code

  • Detect and Track image feature;
  • Perform Ransac PnP algorithm to estimate the transformation between the 3D point cloud on the current frame and their 2D pixel correspondences in the previous frame.;
  • Build a pose graph based on estimated transformations;
  • Update the current pose based on estimated R and t;
  • Build the sparse map;
  • add Bundle adjustment;
  • add loop closure detection with V-BoW;

Python implementation of the Stereo PTAM (parallel tracking and mapping)
Video result, Source Code

  • Multithreads Tracking, Mapping, and Loop Closing;
  • Covisibility Graph (representing the relation between keyframes, mappoints and measurements);
  • Local Bundle Adjustment and Pose Graph Optimization;
  • Motion Model (used for pose prediction, then for reliable feature matching);
  • Point Clouds and Graph visualization;

Master thesis: Stereo-Camera–LiDAR Calibration for Autonomous Driving
Video result, Source Code Master thesis project realized in partnership with Aalto University and Finnish Geospatial Research Institute of Finland.

  • Mono and stereo camera calibration
  • Camera-LIDAR synchronisation
  • Stereo-camera based and LiDAR based 3D reconstruction
  • Camera-LiDAR extrinsic calibration
  • Sensor occlusion handling

Pong game by Panda robots, Robotics AI Lab
Video result, Source Code

  • 2D to 3D projection
  • Forward/Inverse Kinematics with multiple objectives by stacking Jacobians
  • Path planning with Potential Fields
  • Image-based and Position-based visual servoing

Autonomous driving by behavioural cloning
Video result, Source Code

  • Run Udacity car simulation & record some data (images, steering angle, velocity, etc)
  • Data preparation - balance the dataset, augment data
  • Train a regression model (input images, output steering angle)
  • Use the model to control the car steering angle

Small scale autonomous driving car
Simulation, Real car, Source Code

  • Traffic line detection (OpenCV hough transform) & following
  • Traffic sign detection & recognition (CNN model)
  • SLAM with EKF for mapping & localization
  • 2D spline function for path planning
  • Stanley steering and PID controller for path tracking

Reinforcement learning, Agent learns to play pong game, from pixels
Source Code

  • The agent is the green paddle
  • Pass the image through CNN to extract the features
  • Used fully-connected layers to learn the behaviour
  • Used softmax to output 3 actions probabilities (up, down, stay)
  • Policy gradient optimize the weights to maximize the reward

Visual-Odometry
Source Code

  • Detect feature descriptors (harris corner, SIFT)
  • Track the features in 2 consecutive images, using optical flow.
  • Estimate Essential matrix (E) between points correspondences, using RANSAC model fitting.
  • Estimate camera rotation (R) and translation (t) based on essential matrix.
  • Update the current pose based on R and t.

Other projects

Stock-Price-Prediction
Source Code

    Time series prediction
  • The application allows you to upload the current stock prices, and it predicts the future prices
  • It allows the user to train a new model on its own data
  • The app can be automated completely, the agent will buy and sell stocks independently.
  • There are LSTM models implemented in Keras, moving average, RL agents.

Self Hotel - Web app
Source Code
This website provides room registration, self check-In and check-Out services.

    The app is based on:
  • client-side: - Javascript(Bootstrap & jQuery), HTML5,CSS.
  • server-side: - C#, .NET, sql Server.
    SQL:
  • Data description language (DDL).
  • CRUD Operations (create, select, update, delete)
Server/Client-side validation, data manipulation.