Sensor Fusion Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Sensor Fusion
Lino Giefer
Last position:
Senior Data Scientist at VinFast Germany GmbH
- Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
- Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
- Automated extraction and training processes with CI/CD
- Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
- Developed and optimized embedded software for automotive control units
- Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
- Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
- Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
- Developed and trained machine learning models using PyTorch
- Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
- Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
- Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
- Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
- Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)
Achim B.
Last position:
Software Developer / Integration, Test & MBSE
- Integration of ADAS components in different project roles as integrator, tester and debugger, including BMW KAFAS4 camera system and ZF Full Range Radar.
- Connection and visualization of radar sensor data via ROS2 (Linux) to support sensor fusion and...
Omar Tag
Last position:
Founder & Technical Solutions Consultant at TAG Pro
- Engaged by MILLA Group (autonomous shuttle manufacturer) to integrate and harden a safety-critical AD stack toward production: audited the architecture across perception, HD mapping, and positioning, and delivered a gap analysis with remediation roadmap.
- Lead root-cause analysis of sensor failures across a deployed shuttle fleet; shipped remediation in a versioned AD release and drove vehicle-level field validation at multiple operational sites.
- Design and implement interfaces between perception, localization, and vehicle systems in C++; identify integration risks and drive resolution of cross-subsystem technical issues across the AD stack.
- Standardized the client's software development lifecycle by introducing Agile workflows and CI/CD pipelines, shortening integration and validation cycles.
Kartik Trivedi
Last position:
Master Thesis Student at Fraunhofer LBF
- Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
- Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
- Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
- Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
- Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
- Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
- Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
- Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.
Ayusee Swain
Last position:
Intern at Schaeffler
- Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
- Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
- Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.
Kai Wolf
Last position:
Schwarz IT KG
- Migration of the software development process of a medical technology software to C/C++ package manager Conan and development of macOS-specific system components
Yimeng Wang
Last position:
R&D Software Engineer at Advantest
- Development and maintenance of hardware drivers in C++
- Conducting unit and integration tests to ensure code quality
- Debugging and fixing issues with the hardware team and FPGA team
- Defining and developing software concepts and coordinating with the software architect
- Expanding test automation to improve efficiency
- Research and development of algorithms to improve existing codebases (runtime, memory usage, accuracy)
Natalia Pavlovskaia
Last position:
Senior Computer Vision Engineer at Dandy
- Developed point cloud classification and segmentation models for dental applications.
- Designed domain adaptation techniques that improved F1 score by 0.1 on a new clinical domain.
- Worked with 3D geometric data and production-scale ML pipelines.
Tobias Bauernfeind
Last position:
Lead XR Project at BMW Group
- Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
- Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
- Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
- Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
- Oversee a small fleet of test vehicles for validation, tests, and data collection.
Vasco Almeida
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
Daniel Carton
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Ehsan Zibaei
Last position:
Freelance Robotics Software Engineer at Independent Contractor
- Developed and deployed custom ROS2 nodes for real-time LiDAR-IMU fusion using R3Live++ on ARM64 hardware (Docker, CMake)
- Configured IMU, camera, and LiDAR drivers for proper data timing and formats to enable real-time 3D mapping with R3Live
- Built complete simulation stack for quadrotor UAVs: Ardupilot SITL + ROS2/DDS + Gazebo Harmonic + YOLOv8 detection
Rafal Noga
Last position:
Freelance R&D Engineer Control Systems & Optimization at Self-employed
- Providing services, training and developing solutions.
Madhava Narayanappa
Last position:
Function Developer ADAS at Continental Automotive GmbH through Ferchau GmbH
- Project: Sensor fusion application for traffic participant detection
- Software frameworks: C++ (11,14), Python, Visual Studio, MTS, Qt, GitHub, Jenkins, JIRA, Confluence, Conan, CAN, RTOS, DOORS, CMake
- Refactored and adapted sensor fusion algorithms by processing sensor data (camera and radar) for ACC and EBA as per requirements
- Handled system test issue reports in JIRA
- Tuned Kalman filters and introduced new features to enhance tracking
- Adapted architecture, detailed design (UML) and simulation tool (Qt)
- Conducted unit testing, code reviews and static code analysis in compliance with MISRA standards
- Conducted regression testing to validate software, involved in software releases (CI/CD), KPI evaluation by testing NCAP scenarios
- Flashed ADAS software to target vehicles, used UDS protocol and OBD-II tools for diagnostics and verification
Saad Abdullah
Last position:
AI Software Engineer at RoBoTec-PTC
- Built data pipelines with DVC for version control and efficient data management
- Trained and optimized AI models
- Improved CVAT with custom annotation formats, AI model integration, and streamlined annotation workflows
- Trained, debugged, evaluated, and deployed DCNN models in production
- Developed MaDCAT, an AI-powered CVAT extension for simultaneous data capture and annotation
Discover over 15,000 top freelancers
Statistics of experts using Sensor Fusion
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.6 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Automotive, Information Technology, Manufacturing
Certification focus areas
Information Technology, Logistics, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
88%
Doctorate
19%
Certifications per freelancer
1
Most common languages
English, German, Spanish
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Sensor Fusion
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it does
Sensor fusion combines signals from multiple sensors into one consistent view of the world. Teams use it for robotics, ADAS, autonomous machines, industrial inspection and navigation systems where one sensor alone is not enough. Strong work turns noisy inputs into stable position, motion and object estimates.
Typical stacks
- IMU, camera, LiDAR, radar and GNSS alignment
- Time sync, calibration and coordinate transforms
- State estimation with Kalman, EKF or particle filters
- Perception and tracking for edge or embedded systems
- Data fusion pipelines for testing and validation
Where experts help
Companies bring in freelance specialists when a prototype needs to move into a production system. Common tasks include sensor selection, fusion architecture, latency tuning and fault handling. In Germany, this often comes up in automotive, robotics, manufacturing and smart mobility projects.
What strong work looks like
Good sensor fusion work is measurable and traceable. Experts document assumptions, handle missing data, check calibration drift and make the system robust across changing weather, lighting and motion. They also know when to use classic filters and when a learned approach is worth the trade-off.
Related skills
Sensor fusion rarely stands alone. Strong specialists also work with ROS, embedded C++, Python, MATLAB, computer vision, control systems and data logging tools. They understand how sensor timing, frame alignment and inference latency affect the whole stack.
When to hire
Bring in a specialist when readings conflict, tracking is unstable or integration across sensors is slowing the project down. It also helps when a team needs a second opinion on multi-sensor fusion, a safety review or a clean handover before release. For Germany-based teams, remote work is common, with on-site sessions useful for calibration and field tests.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Sensor Fusion.
Sensor fusion is used to merge camera, radar, LiDAR, IMU and GNSS signals into one reliable result. It is common in autonomous systems, robotics, industrial inspection and navigation where the system must keep working when one sensor is noisy or missing data. The goal is better tracking, localization and decision-making.
A strong sensor fusion setup is more robust than a single-sensor approach because it can cross-check inputs and fill gaps. One sensor may be accurate in ideal conditions but weak in rain, glare, occlusion or vibration. Fusion helps the system keep a stable world model when the environment changes.
Sensor fusion usually means combining live sensor signals at the measurement or state level. Data fusion is a broader term and can include sensor inputs, logs, maps and other data sources. In projects, the two terms are often used close to each other, but sensor fusion usually points to real-time engineering work.
A good sensor fusion specialist often works with calibration, time synchronization, coordinate frames and state estimation. Skills in ROS, embedded C++, Python, computer vision and control systems are often useful too. For many projects, understanding hardware limits matters as much as the math.
A sensor fusion project needs someone who has already shipped real systems, not only read about the methods. Simple prototypes may only need focused support, while production systems need deeper work on latency, drift, failover and validation. The more safety-critical the system, the more important proven delivery becomes.
Yes, many sensor fusion tasks can be done remotely, especially design, code reviews, algorithm work and data analysis. On-site time is still useful for sensor mounting, calibration runs and field testing. For Germany-based teams, a mixed setup is often the most practical choice.
Look for clear thinking, not just formulas. A strong sensor fusion freelancer can explain sensor trade-offs, show how they handle noise and missing data, and describe how they validate the system on real logs. Good signs are clean assumptions, testable design choices and careful handling of timing and frames.
In most projects, Sensor fusion is the same idea as multi-sensor fusion or multisensor fusion. The wording changes, but the work still centers on combining inputs from several sensors into one reliable estimate. Search terms may differ, but the engineering problems are usually the same.
The average hourly rate of freelancers in Germany who have used Sensor Fusion in their recent projects is 93 €, which corresponds to a daily rate of about 745 € based on an 8-hour working day.
Of the freelancers in Germany who have used Sensor Fusion in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Germany who have used Sensor Fusion in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Germany who have used Sensor Fusion in their recent projects are English (100%), German (94%), and Spanish (19%).
The most common industries among freelancers in Germany who have used Sensor Fusion in their recent projects are Automotive (81%), Information Technology (81%), and Manufacturing (69%).
The most common business areas among freelancers in Germany who have used Sensor Fusion in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (94%).
Main locations of FRATCH Experts, who have recently used Sensor Fusion
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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