Daniil Nikulin is a C++/Python software engineer with eight years of experience building perception, mapping and simulation systems for autonomous vehicles, UAV photogrammetry, and robotics. He has deep hands-on expertise in 3D reconstruction and point-cloud processing—contributing feature-matching work to the widely used COLMAP project and improving test coverage for the Point Cloud Library—alongside low-level Linux kernel and PCIe data-path development. At Luxoft he maintained and optimized autonomous driving simulation and map conversion pipelines (ASAM OpenDRIVE/OSI, NDS) and helped mature ADAS sensor-cleaning and availability estimation systems. His background blends algorithmic research, distributed real-time processing and production-grade tooling, and he is now aiming to apply that mix to field robotics in agriculture. A less obvious strength is his pattern of improving legacy systems—refactoring, testing and stabilizing complex codebases—to make advanced perception pipelines reliable in real-world deployments.
8 years of coding experience
7 years of employment as a software developer
Master's degree Applied mathematics and information science, Master's degree Applied mathematics and information science at ITMO University
Bachelor's degree Software Engineering, Bachelor's degree Software Engineering at Peter the Great St.Petersburg Polytechnic University
Contributions:9 reviews, 15 commits, 7 PRs in 1 year 3 months
Contributions summary:Daniil primarily focused on enhancing the testing framework for the PCL library. Their commits involve adding new tests for the `CropHull` filter, refactoring existing tests to align with the project's style guide, and fixing issues related to the filter's behavior. They demonstrate proficiency in using Google Test (gtest) and a deep understanding of point cloud data structures and filtering algorithms, ensuring the library's reliability.
COLMAP - Structure-from-Motion and Multi-View Stereo
Role in this project:
Back-end Developer / Algorithm Engineer
Contributions:5 commits, 4 PRs, 3 comments in 1 year 3 months
Contributions summary:Daniil primarily contributed to the feature matching functionality within the COLMAP project, focusing on the implementation and optimization of feature matching algorithms. Their work includes the integration of the FLANN library for efficient nearest neighbor search and the development of functions to compute distance matrices and find best matches. They also added unit tests for feature matching and addressed boundary conditions.
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