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Hilti × Trimble SLAM Challenge 2026 entry built for the 3D Vision course at ETH Zürich — a metric, gravity-aligned, floorplan-consistent trajectory and 3D map from indoor dual-fisheye + IMU data, ranking 3/22 in localization and 7/62 in SLAM.

A comprehensive guide to setting up multiagent simulation environments for PX4 and ArduPilot with Gazebo, covering namespacing, sensor configuration, and external odometry.

Building a pipeline that converts spoken language into continuous sign language motions for a humanoid robot.

This project presents a multi-mission transformer robot capable of operating both as a ground vehicle and a quadrotor drone.

This project implements real-time gesture classification using the uMyo EMG sensors.

Extended an existing Deep Reinforcement Learning navigation framework to support the TurtleBot3 platform with realistic 2D LiDAR observations, and migrated the full system from ROS 1 to ROS 2.

Through a series of collaborative competitions, the Sapience initiative fosters innovation in search and rescue operations, enabling multiple drones to effectively navigate and map GPS-denied environments, detect and deliver aid to victims, and perform co…

This page contains a short collage of some of the hardware projects I worked on over the years, starting with school projects created when I was studying at HTL Mössingerstraße.

HASCY is a remote-controlled sled that drives on a rail that is mounted at the top of a tunnel.

Using either the TFJS Task API or the TFLITE Web API you can now deploy Tensorflow Lite models on the web without even needing to convert them into Tensorflow.js format.

The TensorFlow Lite Model Maker library is a high-level library that simplifies the process of training a TensorFlow Lite model using a custom dataset.

D2Go is a production-ready software system from FacebookResearch, which supports end-to-end model training and deployment for mobile platforms.

TensorFlow.js is a deep learning library providing you with the power to train and deploy your favorite deep learning models in the browser and Node.js.

With the recently released official Tensorflow 2 support for the Tensorflow Object Detection API, it's now possible to train your own custom object detection models with Tensorflow 2.

Learn how to use the Tensorflow Object Detection API with Tensorflow 2

The Arduino Nano 33 BLE Sense is an evolution of the traditional Arduino Nano, but featuring a lot more powerful processor, the nRF52840 from Nordic Semiconductors, a 32-bit ARM® Cortex™-M4 CPU running at 64 MHz.

Use TensorRT to run PyTorch models on the Jetson Nano.

Run Tensorflow model on the Jetson Nano by converting them into TensorRT format.

YOLO Object Detection on the Jetson Nano using TensorRT

The NVIDIA Jetson Nano Developer Kit is a small edge computer for AI development.

Train a custom yolo object detection model in PyTorch

Use and create YOLOV3 models with keras-yolo3.

Use YOLOv3 with OpenCV to detect objects in both images and videos.

Learn how to use YOLO for Object Detection.

Getting started with Mask R-CNN in Keras

Create a custom Mask R-CNN model with the Tensorflow Object Detection API.

Learn how to create a custom instance segmentation model using Detectron2.

The LoRaWAN® specification is a Low Power, Wide Area (LPWA) networking protocol designed to wirelessly connect battery operated 'things' to the internet in regional, national or global networks.

LoRa is a spread spectrum modulation technique derived from chirp spread spectrum (CSS) technology.

Creating math animations in Python with Manim, a mathematical animation engine made by 3Blue1Brown

Use your Tensorflow Object Detection model on edge devices by converting them to Tensorflow Lite.

Deploy your Streamlit application using Heroku, a platform as a service (PaaS)

Interpret PyTorch models with Captum.

Detectron2 is Facebooks new vision library that allows us to easily us and create object detection, instance segmentation, keypoint detection and panoptic segmentation models.

Turn your data science scripts and projects into beautiful websites/dashboards using Streamlit.

One of the most popular languages today is C# which is used for many applications.

Local model interpretation is a set of techniques aimed at answering questions like: Why did the model make this specific prediction?

Global model interpretation is a set of techniques that helps us to answer questions like how does a model behave in general?

The Google Coral Edge TPU allows edge devices like the Raspberry Pi or other microcontrollers to exploit the power of artificial intelligence.

Regardless of what problem you are solving an interpretable model will always be preferred because both the end-user and your boss/co-workers can understand what your model is really doing.

Learn how to create your own object detector using the Tensorflow Object Detection API.

Get started visualizing data in Python using Matplotlib, Pandas and Seaborn

Learn the basics of Keras, a high-level library for creating neural networks running on Tensorflow.

Scrape data from Reddit using PRAW, the Python wrapper for the Reddit API.

Build a system that is able to recommend books to users depending on what books they have already read using the Keras deep learning library.

Generating text in the style of Sir Arthur Conan Doyle using a RNN