LiDAR-Inertial Navigation Companion Integration

@mubeenm5026

As an AI, SaaS, Web & Mobile Development agency, your project is an exciting opportunity for our highly skilled team. We have extensive experience in developing with robotics operating system (ROS) across various industries, including the autonomous navigation and real-time mapping application you're looking for. Our proficiency in Python and C++, along with our deep knowledge of ROS 2 Humble, makes us a perfect fit to build your ROS nodes for LiDAR-Inertial odometry/SLAM. Not only can we provide a clean MAVROS topic bridge for filtered pose updates to your flight controller but our expertise in API development & integrations allows us to ensure seamless and efficient communication between platforms. We will ensure the CPU load on Xavier NX remains below 70%, and the code builds with colcon using only standard ROS 2 dependencies. Furthermore, our experience with documentation and creating repeatable test procedures will guarantee you get detailed instructions to validate the performance in a mock boiler environment. Plus, you can rely on our long-term technical support if any issues arise. With Web Crest, accuracy meets efficiency - let's join forces to create a high-precision mapping solution that exceeds your expectations.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@shadabkhan92

As an AI specialist who lives and breathes autonomous machinery, I am ready to take on the challenge of fusing LiDAR and IMU data for your boiler mapping project. My experience with ROS 2, Python, C++, and a range of robotic systems has equipped me with the necessary skills to tackle this project head-on. To make sure we meet your expectation of minimal drift, I plan to base our strategy on LOAM which is built specifically to handle challenging environments, like the confined and reflective interior of boilers. Moreover, with proficiency in using Xavier NX running Ubuntu 22.04 + ROS 2 Humble and program management through MAVLink for off-board control, I can ensure seamless coordination with the existing stack. Apart from just delivering code and packages, my team aims to provide comprehensive documentation and repeatable testing procedures that not only meet your standard but enable effective future maintenance. If you're looking for more than just an algorithm developer but a partner that ensures successful implementation in real-world scenarios, I believe I'm the right fit for your project.

@Shrutibimpl

Hello, I can build and tune your ROS 2 Humble LiDAR Inertial Odometry package for indoor boiler inspection on your Jetson Xavier NX. My plan is to implement a C++ tightly coupled LIO framework such as FAST LIO2 optimized for ROS 2 Humble. I can configure point cloud filtering to remove high intensity reflections from metallic boiler walls. I can calibrate the IMU time offset and extrinsic transformations between your 360 degree mechanical LiDAR and MEMS IMU. I can build a dedicated ROS 2 node to transform the LIO pose estimate and publish it directly to the MAVROS vision pose topic for PX4 or ArduPilot flight controllers. I can optimize the C++ compilation flags to keep Xavier NX CPU usage well below seventy percent and write colcon build scripts for headless deployment. In a past project I developed a ROS 2 FAST LIO2 pose estimation pipeline for drones inspecting confined industrial spaces, streaming vision pose updates via MAVROS. 1) What specific model of 360 degree mechanical LiDAR and MEMS IMU are mounted on your inspection vehicle? 2) Is your flight controller running PX4 or ArduPilot firmware for the off-board MAVROS integration? 3) What is the average diameter or dimension of the boiler interiors you plan to inspect? Thanks, Bharat

@Qupati

Hi! This project strongly matches my experience in ROS2, mobile robotics, SLAM, sensor fusion, IMU integration, LiDAR navigation, and real-hardware deployment. I’m a Mechatronics Engineer with 5+ years of experience building autonomous robot systems using ROS1/ROS2, Jetson platforms, EKF-based fusion, wheel/IMU odometry, LiDAR mapping, MAVLink-related robotics workflows, and headless Linux deployments. For your boiler inspection vehicle, I’d first calibrate the LiDAR–IMU extrinsics and timing, then benchmark FAST-LIO2 against an alternative such as RTAB-Map or an EKF-assisted pipeline. My likely choice would be FAST-LIO2 because tightly coupled LiDAR-inertial estimation is generally better suited to confined areas where GPS is unavailable and wheel odometry may not exist. I can support: - ROS2 Humble LiDAR-inertial odometry and mapping setup - LiDAR/IMU time synchronization and extrinsic calibration - Tuning for reflective surfaces, narrow geometry, and repeated boiler structures - Filtered pose publishing through MAVROS - CPU profiling and Xavier NX optimization - Colcon workspace, launch files, YAML configuration, and headless startup - Rosbag-based testing, drift measurement, and clear documentation I can begin by reviewing the exact LiDAR and IMU models, message rates, timestamps, mounting transform, MAVROS interface, and available ground-truth method, then build the solution through measurable test milestones. Best regards, Hussein

@ashinnd84

Hello Sir/Madam, we are a team of senior AI ML Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@alluankit01

Hi, I can integrate LiDAR-IMU odometry/SLAM on your Jetson Xavier NX with ROS 2 Humble, tuned for confined boiler inspection environments and connected to the flight controller through MAVLink/MAVROS pose updates. The best solution is to first review your LiDAR model, IMU specs, mounting offsets, calibration data, MAVLink topic needs, rosbag samples, and mock boiler test setup. I’ll then configure a suitable LiDAR-inertial package such as FAST-LIO2 or LIO-SAM-style workflow, tune it for reflective/confined spaces, and publish filtered pose continuously to the flight controller. I’m comfortable with ROS 2, Ubuntu 22.04, Jetson Xavier NX, LiDAR-inertial SLAM, sensor fusion, EKF/UKF concepts, MAVROS/MAVLink bridges, C++/Python nodes, launch/config files, calibration, rosbag testing, and headless deployment. Deliverables include: * ROS 2 LiDAR-IMU SLAM setup * Launch and config files * Sensor calibration support * MAVROS pose bridge * Headless runtime setup * Xavier NX performance tuning * Drift test procedure * Sample rosbag validation * Documentation and handover notes I’ll focus on stable pose estimation, low drift, clean ROS 2 integration, and CPU-efficient operation for industrial boiler mapping. Best regards Ankit

@mcollinscalz

Hi there, my name is Collins and am a Robotics Engineer Achieving less than 5 cm pose drift inside reflective, feature-sparse industrial boilers requires tight LiDAR-IMU coupling and strict intensity filtering. I will build a production-ready, C++ ROS 2 Humble package based on FAST-LIO2 optimized specifically for your Jetson Xavier NX and MAVLink flight controller. Why FAST-LIO2 for this environment? Tight Coupling via IEKF: Directly integrates raw IMU measurements with point-to-plane registration, eliminating Z-axis drift in symmetrical cylinder geometry. Low Computational Overhead: Lightweight C++ implementation runs at under 45% CPU load on Xavier NX (well below your 70% ceiling). Reflective Noise Handling: Custom pre-filtering pipeline to suppress multi-path LiDAR reflections off metallic surfaces. What I Will Deliver: Colcon-Buildable ROS 2 Package: Clean, headless launch files and tuned YAML configs for your 360-degree LiDAR and IMU pair. MAVROS Bridge: Low-latency pose injection to /mavros/vision_pose/pose with properly conditioned covariance matrices to prevent flight controller EKF lane switches. Verification Package: Complete calibration guide, systemd auto-start script, and a recorded rosbag evaluation report proving under 5 cm cumulative drift over 10 minutes. Let's connect! Are you running PX4 or ArduPilot on your flight controller?

@NEHABHAT92

As an experienced developer in Python, I want to assure you that my skills extend far beyond just web and mobile app development. Over the past nine years, I've encountered and successfully resolved a wide range of technical challenges. This project specifically aligns with my skills that include Python, Mobile App Testing in addition to my familiarity with ROS 2, which is a significant aspect of this current job. Moreover, I understand the critical importance of accuracy in your project. My Problem Solving aptitude has always come handy while resolving complex issues under pressing timelines. I am confident in my ability to develop a robust and accurate LiDAR-Inertial odometry/SLAM solution using either FAST-LIO2, LOAM, RTAB-Map, or a custom EKF/UKF-based approach. The choice would be catered towards fulfilling the accuracy requirement you've specified. Lastly, as an individual who's no stranger to autonomous systems like yours, I can anticipate potential hurdles that might arise along the way of developing such a system and provision for them beforehand. My dedication and commitment to providing high-quality deliverables with a sustainable support system make me an ideal candidate for this project. I'm really excited by the opportunity to dive into the specifics of this project with you and provide you with an exceptional solution.

@tshibsamuel477

Hi there, I read your offer carefully and understand that you need LiDAR-IMU fusion on a Jetson Xavier NX with ROS 2 Humble, focused on high-accuracy positioning inside industrial boilers with minimal drift. I can support the ROS 2 SLAM/odometry package setup, LiDAR-IMU calibration, FAST-LIO2/LOAM/RTAB-Map evaluation, EKF filtering, MAVROS pose bridge, launch/config files, rosbag testing, headless deployment, and documentation. My experience with robotics, ROS 2, sensor fusion, LiDAR/IMU systems, MAVLink integration, embedded Linux, and autonomous navigation fits this project well. Just send me a message and we can review the sensor specs, calibration data, and inspection environment. Best regards, Samuel Tshibangu

@winston052790

Hello, I am Winston, and I am seeking a candidate proficient in Python, Ubuntu, Documentation, Embedded C++, and Sensor Fusion for LiDAR-Inertial Navigation Companion Integration. In need of ROS 2 nodes or a package set for accurate positioning in industrial boilers, with specific requirements outlined. Please provide a solution with ≤5 cm drift, low CPU load, and code compliant with ROS 2 standards. Looking forward to your expertise in meeting these criteria. Thank you.

@davidt006

HI-I'd like to help with your LiDAR–IMU SLAM project. Your requirements match my background in mathematics, engineering, and software development, where analytical thinking and precision are essential. With a degree in Mathematics and engineering experience performing aerodynamic analysis on airfoils, I'm experienced in solving complex technical problems and working with sensor-based systems. As a Software Developer, I build reliable applications using Python, C++, ROS 2, Git, and modern development tools. For this project, I would recommend FAST-LIO2 as the foundation because of its tightly coupled LiDAR–IMU fusion, high localization accuracy, and efficient performance on embedded platforms like the Jetson Xavier NX. The solution will include: ROS 2 Humble compatible LiDAR–Inertial SLAM package. Sensor calibration, launch, and configuration files. MAVROS bridge publishing filtered pose to the flight controller. Buildable with colcon using standard ROS 2 dependencies. Complete setup, testing, and deployment documentation. Repeatable validation procedure with a sample rosbag. Before getting started, I'd like to clarify: LiDAR and IMU models PX4 or ArduPilot flight controller Time synchronization method Existing LiDAR–IMU calibration Boiler surface conditions (dust, reflections, smoke) I look forward to discussing your setup and delivering a robust localization solution that meets your accuracy goals.

@Kredbird

Hello, I have carefully reviewed your requirements and understand that you need a high-accuracy LiDAR-IMU sensor fusion solution for ROS 2 running on a Jetson Xavier NX, with reliable pose estimation and minimal drift inside challenging industrial boiler environments. I have experience with ROS 2, Python, C++, MAVROS/MAVLink, sensor fusion, SLAM, and robotic navigation, and can develop a robust solution using frameworks such as FAST-LIO2, LOAM, or RTAB-Map, depending on your sensor characteristics and accuracy requirements. I can deliver a complete package including ROS 2 nodes, calibration and launch files, MAVROS integration, tuning for confined environments, detailed documentation, and a repeatable validation procedure with rosbag testing. My focus will be on achieving stable localization, clean code, and a production-ready implementation that meets your performance and deployment requirements. So I am sure I can complete this project perfectly as you want. Please send me a message so that we can discuss more. Thanks, Kyle.

@sks379

Thanks for sharing the detailed requirements. The target of ≤5 cm drift over a 10-minute inspection is achievable, but I'd like to clarify a few details so I can select and tune the best LiDAR-Inertial SLAM pipeline. 1. What are the exact LiDAR and IMU models (manufacturer, model, scan rate, and IMU frequency)? 2. Are the LiDAR and IMU hardware time-synchronized, or do they rely on software timestamps? 3. Is the LiDAR intrinsically integrated with the IMU (e.g., Livox/Ouster) or are they separate sensors? 4. What flight controller are you using (PX4 or ArduPilot), and are you already using MAVROS/MAVLink2? 5. Do you have an existing LiDAR-IMU extrinsic calibration, or should calibration be included in the deliverables? 6. Is GNSS unavailable during inspection, or is it available before entering the boiler? 7. Could you share a short sample ROS 2 bag (1–3 minutes) from the boiler or a similar environment for parameter tuning? 8. Are there strong reflective metal surfaces, steam, dust, or other conditions that affect LiDAR returns? Based on your requirements, I would recommend FAST-LIO2 as the core LiDAR-Inertial Odometry engine because it provides excellent accuracy, low computational load on the Jetson Xavier NX, and performs well in confined environments. I would combine it with loop closure and map optimization to minimize long-term drift, then publish the filtered pose to the flight controller through a MAVROS bridge.

@Myteam007

So right from design to lidar to autonomous, to development and production. Our engineers and machines are lined up for same. With end to end expertise for complete turnkey solution. We will be associated for long term managing the entire technology, as required. Kindly open chat to discuss further. Regards Prateek

@Webslinger01

Your boiler inspection problem is exactly the kind of thing I enjoy—tight spaces, reflective surfaces, and a hard accuracy number to hit. The ±5 cm drift over 10 minutes is achievable, but it needs the right algorithm and careful tuning, not just a default package. I’ve got a clear idea of how to get you there. Here's what I bring to this: → I’ve deployed FAST-LIO2 in a production environment with a 360° LiDAR and MEMS IMU, and I’ve tuned it to beat a 5 cm drift target in a confined, feature-sparse space—so I know the exact parameters that matter. → I’ve built MAVROS bridges for off-board control on a Jetson → I can handle the entire stack myself—ROS 2 nodes, config, calibration, and even the PCB or 3D design for any custom mounts you might need, and I’ll ship you a working prototype. → I have a modular test rig I’ve used for similar odometry validation My approach: → First, I’ll get FAST-LIO2 running on your Xavier NX with your specific sensor pair, and we’ll do a thorough calibration of the LiDAR-IMU extrinsics and time offsets. → Next, I’ll tune the odometry for the boiler’s reflective surfaces, build the MAVROS bridge for continuous pose updates, and make sure the CPU load stays under 70%. → Finally, I’ll run a mock boiler test, capture a rosbag to prove the drift is within spec, and deliver the full documentation and test procedure. Happy to walk you through my approach and past work on a chat—let’s get this moving.

@soniaer987

I’ve spent a lot of time thinking about your boiler inspection problem, and the ±5 cm drift target over 10 minutes in that kind of reflective, confined environment is a really challenging and interesting constraint. I’m excited about the chance to help you get this navigation stack working reliably. Here's what I bring to this: → I’ve built LiDAR-inertial odometry systems using FAST-LIO2 on ROS 2, and I’ve worked on a few projects where the environment was just as harsh—tight spaces and tricky surfaces—so I know where the tuning pitfalls are. → My experience with MAVLink and off-board control means I can get that MAVROS bridge set up cleanly, giving your flight controller a steady stream of filtered pose updates without any hiccups. → I can handle the whole stack myself—from writing the ROS 2 nodes to creating the calibration files and even designing the 3D-printed test fixtures if you need them, and I’ll ship you a complete prototype setup. → I have a set of similar sensor drivers and launch files from past work that I can adapt, so we won’t be starting from a blank page. My approach: → Next, I’ll tune the odometry parameters specifically for the boiler’s reflective surfaces and confined geometry, and build the MAVROS bridge to feed the pose data to your flight controller. For this scope, the cost would be 12500. I’m happy to walk you through my approach and past work on a chat, and we can dive right in.

@sakthivinnavan1

Hello! I'm a Robotics Engineer from Madurai, Tamil Nadu, with expertise in ROS 2, autonomous mobile robots, LiDAR-IMU fusion, and SLAM. I can develop a ROS 2 Humble LiDAR-Inertial SLAM solution optimized for industrial boiler inspections using **FAST-LIO2**, chosen for its high accuracy, low drift, and efficient CPU usage on Jetson Xavier NX. The system will provide continuous filtered pose updates to the flight controller through MAVROS. Deliverables: * ROS 2 Humble package (C++) * LiDAR-IMU calibration and configuration files * Tuned launch files * MAVROS pose bridge * Headless, colcon-buildable workspace * Setup, calibration, and testing documentation * Sample rosbag and validation procedure To achieve the required accuracy, I'll implement precise sensor synchronization, IMU motion compensation, reflective surface filtering, degeneracy detection, and EKF-based pose smoothing. I have experience with ROS 2 Humble, Jetson Xavier NX, MAVROS, Nav2, FAST-LIO2, RTAB-Map, and LiDAR/IMU calibration. To begin, I'll need your LiDAR and IMU models, IMU rate, time-sync details, boiler dimensions, inspection speed, and a 30-60 second rosbag. I'll then provide the implementation plan, expected accuracy, and project timeline.

@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 time synchronization to maintain accuracy in mapping and pose estimation. I'd start by ensuring the timestamps of both data sources are aligned for effective sensor fusion. Can you share access to the data logs so I can analyze the synchronization?