LiDAR-Inertial Navigation Companion Integration

@vishal1145

Your MAVROS bridge will drop pose updates if the LiDAR-Inertial odometry node publishes faster than 50 Hz without a rate limiter, causing the flight controller to revert to GPS and lose position lock inside the boiler where GPS is unavailable. Quick questions - what is the scan rate and point density of your 360° LiDAR? And does your MEMS IMU support hardware time-sync with the LiDAR, or will we need software interpolation to align timestamps? Here is the architectural approach: - SENSOR FUSION: Implement FAST-LIO2 with tightly-coupled Kalman filter because it handles reflective surfaces better than LOAM and runs at 15 Hz on Xavier NX under 60% CPU. - MAVROS INTEGRATION: Build a pose relay node that downsamples odometry to 30 Hz, converts from ROS ENU frame to MAVLink NED, and publishes VISION_POSITION_ESTIMATE messages with covariance tuning. - DOCUMENTATION: Deliver launch files with pre-tuned noise parameters for confined spaces, a calibration script for extrinsic LiDAR-IMU alignment, and a rosbag validator that flags drift over 5 cm. I've built similar SLAM pipelines for 2 autonomous inspection platforms operating in GPS-denied refineries where drift tolerance was under 3 cm. Let's schedule a 20-minute call to review your sensor datasheets and confirm the MAVLink message structure your flight controller expects.

@Qupati

This project is a strong match for my background in ROS2, LiDAR mapping, IMU fusion, localization, TF, MAVLink integration, and real robot deployment. I’m a Mechatronics Engineer with 5+ years of experience working on autonomous robots using ROS1/ROS2, Jetson platforms, LiDAR, IMU, wheel odometry, EKF-based fusion, and SLAM. For this system, I would start with sensor time synchronization, LiDAR–IMU extrinsic calibration, frame validation, and rosbag analysis before tuning the SLAM pipeline. My preferred starting point is FAST-LIO2 because its tightly coupled LiDAR-inertial estimation is well suited to GPS-denied, confined environments. I would also evaluate whether reflective surfaces and repetitive boiler geometry require additional filtering, loop closure, or pose-graph correction. I can support: - ROS2 Humble LiDAR-inertial odometry and SLAM integration - Sensor timestamp and extrinsic calibration - Point-cloud filtering for reflective and confined environments - TF and coordinate-frame validation - MAVROS pose bridge for continuous filtered updates - Xavier NX CPU profiling and headless optimization - Colcon workspace, launch files, YAML configs, and documentation - Repeatable drift-testing procedure with rosbag evidence I’ve previously worked on odometry drift, IMU integration, LiDAR mapping, TF alignment, navigation, and real-hardware debugging. Best regards, Hussein

@alluankit01

Hi, I can integrate LiDAR-IMU odometry/SLAM on your Jetson Xavier NX with ROS 2 Humble and connect the filtered pose output to your flight controller through MAVROS/MAVLink. The best solution is to first review the LiDAR model, IMU specs, mounting offsets, calibration data, rosbag samples, and MAVLink topic requirements. I’ll then configure and tune a suitable ROS 2 LiDAR-inertial pipeline such as FAST-LIO2/LIO-SAM-style workflow for confined, reflective boiler environments. I’m comfortable with ROS 2, Ubuntu 22.04, Jetson Xavier NX, LiDAR-inertial SLAM, sensor fusion, C++/Python nodes, MAVROS bridges, calibration files, launch/config setup, rosbag validation, and headless deployment. Deliverables include: * ROS 2 LiDAR-IMU SLAM setup * Launch and config files * Sensor calibration support * MAVROS pose bridge * Drift test procedure * Xavier NX performance tuning * Sample rosbag validation * Step-by-step documentation I’ll focus on stable pose estimation, low drift, clean ROS 2 integration, and CPU-efficient operation. Best regards Ankit

@mikes126

Hi there ! Your setup is already solid, and I can help integrate reliable LiDAR inertial odometry on the Xavier NX. I'd use FAST LIO2 because it delivers accurate mapping with efficient performance. I'll tune the pipeline, configure MAVROS, and provide calibration, launch files, and a repeatable testing guide. Everything will build with ROS 2 Humble and stay easy to maintain. Budget : ₹1,200/hr Timeline : 5 days

@shah812

Done similar tasks in Lidar https://www.freelancer.com/projects/php/Lidar I am an artificial intelligence expert with more than 12 years of company work experience, deep experience, and strong abilities in various fields of artificial intelligence such as computer vision, machine learning, deep learning, and Image processing(OpenCV, YOLO, SSD, OCR, CNN, RNN). Your project matches my role and I have sufficient ability to complete your project perfectly in a short time. Full stack Developer expert in Wordpress development, Software development, Software architecture, HTML, CSS, JavaScript, Java, PHP, Python, Jquery, React, React Native, Vue.js, Selenium with Python Web Development and Web Design

@bhargav922002

Hello, I'm bharghav, with 10 years of experience in skillfully matching job requirements. My expertise in Python makes me well-suited for developing robust navigation solutions. I understand your need for highly accurate LiDAR-Inertial odometry/SLAM on a Jetson Xavier NX for boiler inspection, specifically targeting sub-5cm drift. I can develop a Python-based ROS 2 package, potentially leveraging FAST-LIO2 or a custom EKF, to achieve precise pose estimation in confined, reflective environments. This will include calibration, MAVROS integration, and comprehensive documentation. Let's connect in chat to discuss this further and elaborate on a tailored approach. Best regards,

@todayintech

I’m excited about the opportunity to integrate your LiDAR and IMU data on an off-board companion computer! This project perfectly aligns with my experience in data fusion and full-stack development. ? Direct Action Plan & Timeframe: I will start with a thorough assessment of your existing data structures, followed by implementing a streamlined data fusion algorithm within 5 days. After that, I will test the integration with your vehicle's navigation system, ensuring optimal performance within 10 days. ? Why Generic Freelancers Fail vs My Approach: Many freelancers lack the specialized knowledge in sensor fusion required for this task. My unique combination of skills in Python and real-time data processing ensures a robust solution, tailored specifically to meet your needs. ✅ Key Deliverables: Step 1: Analyze existing LiDAR & IMU data formats. Step 2: Develop and implement the data fusion algorithm. Step 3: Test and optimize the integration for seamless vehicle navigation. ? Ready to elevate your project? Reply 'READY' to receive an initial breakdown within 12 hours. Let’s make this integration a success! todayintech

@connorh19

? THERE’S ONE THING IN YOUR PROJECT DESCRIPTION THAT IMMEDIATELY STOOD OUT. Achieving a drift of no more than ±5 cm in the challenging environment of industrial boilers requires careful consideration of sensor calibration and environmental factors, which can often be underestimated. I recently completed a similar project, integrating LiDAR and IMU data for autonomous navigation in a confined space, achieving a cumulative drift of just 3 cm over extended runs, ensuring the reliability of the mapping process. I understand your primary goal is high-accuracy positioning. I would approach this by leveraging FAST-LIO2 to optimize the fusion of LiDAR and IMU data, ensuring robust performance in reflective environments. The implementation will include fine-tuning for your specific hardware and extensive testing. My focus will be on delivering practical solutions that ensure long-term success and reliable execution throughout the integration process. I look forward to discussing how I can help you achieve exceptional results. The difference between an average result and an exceptional one is usually decided before the work even begins. Regards Connor

@robosoft11

Hi, I have a strong background in Automation & Robotics, ROS/Linux environments, AI systems, and hardware-software integration, making me well-suited for your LiDAR-IMU fusion project. During my 6-month internship, I worked on AI-driven systems, stress testing, and Linux-based development while collaborating on mission-critical projects. I can implement and tune a ROS 2 LiDAR-Inertial Odometry/SLAM pipeline (FAST-LIO2, LOAM, or another suitable approach), integrate filtered pose output with MAVROS/MAVLink, and provide calibration, launch files, documentation, and repeatable test procedures. Skills: ROS 2, Ubuntu 22.04, C++, Python, Linux, Docker, Robotics, Sensor Integration, AI, Hardware-Software Integration. I focus on clean, maintainable solutions and thorough testing to achieve reliable localization with minimal drift. I'd be happy to discuss your sensor specifications and recommend the best SLAM approach for your boiler inspection system.

@brijitm4

Hi there! I am a robotics developer experienced with Python and NVIDIA Jetson hardware. I can build your ROS 2 LiDAR-IMU sensor fusion nodes for the Xavier NX to ensure under 5cm drift. Let's connect!

@eternitech

Hi, New on Freelancer — 20 years of development experience behind us. We're taking our first few projects here at a fraction of our normal rate purely to build our review history. You get senior agency work at junior pricing; we get a review. Straight trade. integrating LiDAR and IMU data often requires precise calibration to ensure accurate mapping and pose estimation. I would start by examining the synchronization between your sensors and the data processing pipeline on your Ubuntu system. Can you share more details about the current data flow setup?

@aydont

YOU ONLY NEED ONE DEVELOPER WHO GETS IT RIGHT THE FIRST TIME. HERE'S WHY I'M THAT DEVELOPER. I recently helped a client transform their complex navigation challenge into a reliable solution that provided accurate mapping and positioning. I'd love to do the same for you and deliver something that's built to perform from day one. We specialize in integrating advanced sensor technologies for high-accuracy positioning in demanding environments. I understand you need ROS 2 nodes for LiDAR-Inertial odometry tuned for the reflective interiors of boilers. I can ensure the system is clean, professional, and user-friendly while maintaining the required accuracy. We have 75+ 5-star reviews on similar projects and rank in the top 1% among 75 million users. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Aydon.

@DeanSaliegh

I can help you achieve precise mapping and solid pose estimation for your navigation inside industrial boilers. The requirement for less than ±5 cm drift during inspections is clear, and I'm ready to tackle it. I noticed your stack includes the Jetson Xavier NX and ROS 2, which I’m comfortable working with. I can develop ROS 2 nodes in either C++ or Python tailored for your specific conditions. My approach will include efficient data fusion and a robust MAVROS topic bridge for seamless communication with your flight controller. I have done similar work: Modern Real Estate Investment Website - [Link]. My experience in structuring complex data streams will ensure your project runs efficiently and meets the accuracy target. Let’s chat about the details. I can provide a free consultation to discuss the best approach for your needs. Regards, Dean Saliegh

@davide524

Hi, I have worked on similar embedded robotics and sensor integration projects where the focus was combining sensor data, implementing real-time localization, and building reliable communication between companion computers and autonomous systems. I understand this project requires LiDAR-IMU fusion on Jetson Xavier NX with ROS 2 Humble, accurate SLAM, and stable pose output for industrial inspection environments. My approach: • Analyze LiDAR and IMU characteristics and configure suitable calibration parameters. • Implement LiDAR-Inertial odometry/SLAM using approaches such as FAST-LIO2, LOAM, RTAB-Map, or EKF-based fusion. • Develop ROS 2 nodes/packages with proper launch and configuration files. • Integrate MAVROS topics to provide filtered pose estimates to the flight controller. • Optimize processing performance for Jetson Xavier NX and headless operation. • Tune algorithms for challenging environments with reflective and confined surfaces. • Provide rosbag-based testing workflow and accuracy validation procedure. • Deliver documented source code with build instructions using colcon. I have 7+ years of experience with embedded systems, C/C++, Python, sensor integration, robotics software, and IoT platforms. I focus on building reliable real-time systems with clean architecture, performance optimization, and proper documentation. Best Regards

@morne01

We've recently helped a client successfully integrate their navigation systems with high-accuracy mapping capabilities. I can help you achieve precise LiDAR and IMU data fusion for your vehicle, ensuring reliable mapping of industrial boilers. Your focus on maintaining a drift of no more than ±5 cm during inspections shows a commitment to accuracy. I can create clean, professional, user-friendly ROS 2 nodes tailored for your specific setup. With expertise in ROS 2 development, I have delivered similar projects that align with your requirements. We have 75+ 5-star reviews on similar projects and rank in the top 1% among 75 million users. I look forward to discussing this project further. Regards, MORNE.