Igor Bogoslavskyi
Lead, World Representation And State Estimation
Küsnacht (ZH), Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolIgor Bogoslavskyi is a lead robotics researcher and software engineer with 13 years of experience specializing in LiDAR-based scene understanding, mapping, and state estimation for autonomous systems. He combines deep academic training (PhD-level work in photogrammetry and geoinformation robotics) with industry leadership roles at RAI Institute, Magic Leap, Apex.AI and BMW, driving perception and autonomy efforts from research to production. A prolific open-source maintainer in C++ and Python, he ranks among the top contributors in Germany and the top 1% globally for both languages, and is known for projects like EasyClangComplete and the widely used depth_clustering LiDAR library. Based in Zurich, he balances hands-on systems engineering and algorithmic innovation with public education via a technical YouTube channel, demonstrating a rare mix of performance optimization, tooling craftsmanship and clear communication.
13 years of coding experience
10 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at The University of FreiburgDoctor of Philosophy (PhD) Photogrammetrie and geoinformation robotics, Doctor of Philosophy (PhD) Photogrammetrie and geoinformation robotics at The University of BonnBachelor's degree Applied Mathematics, Bachelor's degree Applied Mathematics at Taras Shevchenko National University of KyivHigher basic education certificate Mathematics Physics, Higher basic education certificate Mathematics Physics at Lyceum 145, Kyiv
English, Russian, German, Ukrainian
Stackoverflow
758reputation
27kreached
12answers
16questions
Github Skills (16)
c-language10
cprogramming-language10
image-processing10
computer-vision9
test-driven-design8
opencv8
dijkstra7
ide6
configurations6
std6
android-recyclerview6
android6
android-fragments6
android-coordinatorlayout6
android-layout6
Programming languages (14)
Github contributions (5)
PRBonn/depth_clustering
Nov 2016 - Nov 2021
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 106 commits, 23 PRs in 5 years
Contributions summary:Igor primarily focused on refactoring and improving the depth clustering library. They implemented features to handle radians using literals, made several performance optimizations to the precomputed diff helper, and modified the code to allow for better integration. The user also corrected a depth fix within the Frank Moosmann dataset. The user also added a new function to visualize beta.
pythonsensorvelodynepoint-cloudreal-time
cpp-for-yourself/lectures-and-homeworks
Aug 2022 - Jan 2023
📚 A place for all supplementary materials like slides, homework assignments etc.
Contributions:1 review, 41 commits, 158 PRs in 6 months
placecppcmakeclanghomework-assignments
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