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Optimized DBSCAN clustering for LiDAR Application - pitch version

Nowadays self driving cars rely on many different sensors to percept the surrounding environment. The most relevant one is the LiDAR, which is exploited for mapping, localization, obstacle detection and so on. All those application can use clustering techniques in order to achieve better results. There exist several state of the art algorithm for clustering, for example K-Means, Hierarchical and DBSCAN, however none of them are compliant to real time requirements. This latter aspect is fundamental for autonomous vehicles, which can be seen as real time systems that need to respect deadlines.
Our solution is an optimized clustering algorithm, which exploits parallelism and a clever point selection to massively drop computation times. This solution allows us to perform clustering online while fulfilling our real time needs.

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Optimized DBSCAN clustering for LiDAR Application - pitch version

  1. 1. Bartoli Luca San Francisco - 15 May 2019 LiDAR clustering Bartoli Luca - 228618@studenti.unimore.it Modena - 15/05/2019 1
  2. 2. Bartoli Luca San Francisco - 15 May 2019 Clustering 2
  3. 3. Bartoli Luca San Francisco - 15 May 2019 HiPeRT Prototype 3
  4. 4. Bartoli Luca San Francisco - 15 May 2019 Timing performance 4 Dataset with 40’000 AVG points for spin
  5. 5. Bartoli Luca San Francisco - 15 May 2019 Tracking 3D 5
  6. 6. Bartoli Luca San Francisco - 15 May 2019 Classification 6
  7. 7. Bartoli Luca San Francisco - 15 May 2019 7 Demo
  8. 8. Bartoli Luca San Francisco - 15 May 2019 8 Luca Bartoli - 228618@studenti.unimore.it Thanks for your attentions https://hipert.unimore.it/

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