This paper presents $\rho$LiRLo, a LiDAR-based Relative Localization method, designed for reliable robot navigation and control in GNSS-denied environments. $\rho$LiRLo enhances point cloud processing using intensity filtering with a retro-reflective marker. The marker's position is determined via Euclidean clustering, while a Kalman filter tracks the robot's pose. To improve localization accuracy in dynamic conditions, IMU measurements are integrated, and a robotic manipulator actively tracks the marker, expanding LiDAR's field of view. The method is demonstrated on an Unmanned Aerial Vehicle (UAV) in both indoor and outdoor experiments. Indoor tests benchmark localization against OptiTrack motion capture, while outdoor experiments are conducted in a maritime environment with the tracking system mounted on an Unmanned Surface Vehicle (USV). To mitigate the challenges of dynamic sea conditions, IMU measurements from the floating base of the USV are used to compensate for disturbances introduced by waves and wind. $\rho$LiRLo demonstrates high accuracy, low-latency feedback, and strong potential for applications in GNSS-denied settings.


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    Title :

    $\rho$LiRLo: LiDAR-Based Relative Localization with Retro-Reflective Marker




    Publication date :

    2025-05-14


    Size :

    1048865 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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