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Conference papers

6 channels Velodyne versus planar LiDARs based perception system for Large Scale 2D-SLAM

Abstract : The ability of self-localization is a basic requirement for an autonomous vehicle, and a prior reconstruction of the environment is usually needed. This paper analyses the performances of two typical hardware architectures that we evaluate in our 2D Simultaneous Localization and Mapping (2D-SLAM) system for large scale scenarios. In particular, the selected configurations are supposed to guarantee the possibility of integrating at a later stage mobile objects tracking capabilities without modifying the hardware architecture. The choice of the perception system plays a vital role for building a reliable and simple architecture for SLAM. Therefore we analyse two common configurations: one based on three planar LiDARs Sick LMS151 and the other based on a Velodyne 3D LiDAR VLP-16. For each of the architectures we identify advantages and drawbacks related to system installation, calibration complexity and robustness, quantifying their respective accuracy for local-ization purposes. The conclusions obtained tip the balance to the side of using a Velodyne-like sensor facilitating the process of hardware implementation, keeping a lower cost and without compromising the accuracy of the localization. From the point of view of perception, additional advantages arise from the fact of having 3D information available on the system for other purposes.
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Contributor : Philippe Martinet Connect in order to contact the contributor
Submitted on : Wednesday, January 29, 2020 - 1:01:12 PM
Last modification on : Wednesday, April 27, 2022 - 3:47:52 AM
Long-term archiving on: : Thursday, April 30, 2020 - 3:42:38 PM


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  • HAL Id : hal-02459368, version 1


Simona Nobili, Salvador Dominguez-Quijada, Gaëtan Garcia, Philippe Martinet. 6 channels Velodyne versus planar LiDARs based perception system for Large Scale 2D-SLAM. 7th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Sep 2015, Hamburg, Germany. ⟨hal-02459368⟩



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