Localization for Autonomous Driving is mainly researched on camera and LiDAR data, rarely on radar data. Applying a point cloud pose graph SLAM on a modern 77GHz automotive radar sensor-set, we realize direct radar sub-map scan matching for consistent mapping and ego vehicle localization at low speeds in parking scenarios. A spatiotemporal activation maps pre-filters clutter and noise. Matching locally overlapping radar sub-maps in open-loop sections results in a locally stiffer but drift corrected pose graph architecture. Integrating point cloud segmentation of 3d radar detections yields semantically consistent scan matching and increased static map accuracy. Our results reveal how semantic information, temporal filtering, and Normal Distribution Matching can outperform the accuracy of direct radar mapping and localization. Our approach SERALOC yields a new benchmark of 0.4 m absolute pose error (APE) over a distance of 624 m, measured on real-world test data against differential GPS data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SERALOC: SLAM on semantically annotated radar point-clouds


    Beteiligte:


    Erscheinungsdatum :

    2021-09-19


    Format / Umfang :

    2262095 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds

    Dimitrievski, Martin / Veelaert, Peter / Philips, Wilfried | IEEE | 2017


    Learning Semantics on Radar Point-Clouds

    Isele, Simon T. / Klein, Fabian / Brosowsky, Mathis et al. | IEEE | 2021


    GENERATING POINT CLOUDS BASED UPON RADAR TENSORS

    KUSCHK GEORG / CANALS MARC UNZUETA / MÖLLER SVEN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Landmark based radar SLAM using graph optimization

    Schuster, F. / Keller, C. G. / Rapp, M. et al. | IEEE | 2016


    Simulation of automotive radar point clouds in standardized frameworks

    Eder, Thomas / Technische Universität München / Eric Cuvillier, Firma | TIBKAT | 2021