Often, the SLAM problem is viewed as one of the keys to truly realizing self-driving robots. Therefore, the SLAM problem is also the core technology of self-driving cars. However, many problems make it impossible for SLAM algorithms to drive vehicles for hundreds of kilometers under very different conditions. There are two main problems dealing with SLAM for self-driving cars: (1) localization drifts over time and (2) maps may not necessarily satisfy every driving condition. The former problem is well known in the SLAM community that as the distance travelled by the car increases, the localization estimate given by the SLAM algorithm will deviate the car from the true trajectory; without prior knowledge, correct localization is not even guaranteed within a few kilometers. The second question, regardless of the conditions, needs to have a map sufficient for the positioning task. In this chapter, we mainly see how SLAM is being applied to autonomous driving and then we focus on the high-definition map creation.


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    Titel :

    SLAM in Autonomous Driving


    Beteiligte:
    Ren, Jianfeng (Autor:in) / Xia, Dong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    10.08.2023


    Format / Umfang :

    26 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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