2026-04-13T15:08:04Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4470072025-12-06T05:46:41Zcom_2072_1033col_2072_452951

LiDAR Inertial Odometry SLAM for Formula Student Vehicles Perez Ruiz, Carlos Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Garrell Zulueta, Anais Solà Ortega, Joan Àrees temàtiques de la UPC::Informàtica::Robòtica Robòtics Computer vision Kalman filtering Mapeig i Localització Simultànea LiDAR Odometria Inercial amb LiDAR iOctree teoria de Lie IMU LiDAR Intertial Odometry Lie theory LiDAR Robòtica Visió per ordinador Kalman, Filtratge de This thesis presents LIMOncello, a novel LiDAR-Inertial Odometry (LIO) Simultaneous Localization and Mapping (SLAM) algorithm tailored for the Formula Student autonomous competition context. Originating from the successful adaptation of the FAST-LIO framework, LIMOncello integrates an Iterated Error-State Kalman Filter (IESKF) for robust trajectory estimation, combined with efficient point cloud processing strategies including voxel-grid downsampling and advanced deskewing techniques.\\ Special attention has been given to the optimized data structures and computational efficiency, leveraging an innovative incremental Octree implementation to significantly enhance real-time performance. Validated through extensive testing on the CAT17x Formula Student vehicle, equipped with an Ouster OS1-64 LiDAR, the algorithm demonstrates improved accuracy, reliability, and processing speed, achieving sub-20-millisecond computation times. While developed within the competitive Formula Student environment, LIMOncello's versatility and robustness also make it suitable for broader applications in autonomous robotics and real-time navigation tasks. 2025-07-01 Bachelor thesis https://hdl.handle.net/2117/447007 197445 http://hdl.handle.net/2117/447007 eng Restricted access - confidentiality agreement application/pdf Universitat Politècnica de Catalunya