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Optimized DBSCAN clustering for LiDAR Application - talk 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 - talk version

  1. 1. Bartoli Luca San Francisco - 15 May 2019 DBSCAL Clustering Bartoli Luca - 228618@studenti.unimore.it Modena - 15/05/2019 1
  2. 2. Bartoli Luca San Francisco - 15 May 2019 Environment perception 2 ? ? ? ? ● Principal case in autonomous driving ● Sensors help us to percept the environment near the car
  3. 3. Bartoli Luca San Francisco - 15 May 2019 Environment perception 3 LIDAR RADAR CAMERA LiDAR Radar Camera
  4. 4. Bartoli Luca San Francisco - 15 May 2019 Field of view 4 Data based on the common sensors present on the market LiDAR Radar Camera
  5. 5. Bartoli Luca San Francisco - 15 May 2019 Distance 5 Data based on the common sensors present on the market LiDAR Radar Camera
  6. 6. Bartoli Luca San Francisco - 15 May 2019 Night time 6 Data based on the common sensors present on the market LiDAR Radar Camera
  7. 7. Bartoli Luca San Francisco - 15 May 2019 Weather conditions 7 Data based on the common sensors present on the market LiDAR Radar Camera
  8. 8. Bartoli Luca San Francisco - 15 May 2019 HiPeRT Prototype 8
  9. 9. Bartoli Luca San Francisco - 15 May 2019 Real time conditions 9
  10. 10. Bartoli Luca San Francisco - 15 May 2019 Real time conditions 10 < 40
  11. 11. Bartoli Luca San Francisco - 15 May 2019 Clustering 11
  12. 12. Bartoli Luca San Francisco - 15 May 2019 Timing performance 12 Dataset with 40’000 AVG points for spin
  13. 13. Bartoli Luca San Francisco - 15 May 2019 Timing performance 13 Dataset with 40’000 AVG points for spin < 40
  14. 14. Bartoli Luca San Francisco - 15 May 2019 Clustering algorithms 1. K-Means Clustering 2. Hierarchical Clustering 3. DBSCAN 14
  15. 15. Bartoli Luca San Francisco - 15 May 2019 K-Means Clustering 15 ● k-means clustering aims to partition n observations into k clusters.
  16. 16. Bartoli Luca San Francisco - 15 May 2019 K-Means Clustering 16 ● k-means clustering aims to partition n observations into k clusters. ● Need to know the number of clusters!
  17. 17. Bartoli Luca San Francisco - 15 May 2019 Hierarchical Clustering ● Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy (Agglomerative) 17
  18. 18. Bartoli Luca San Francisco - 15 May 2019 Hierarchical Clustering ● Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy (Agglomerative) ● o(n3 ) 18
  19. 19. Bartoli Luca San Francisco - 15 May 2019 DBSCAN ● Given a set of points in some space, it groups together points that are closely packed together. 19
  20. 20. Bartoli Luca San Francisco - 15 May 2019 DBSCAN ● Given a set of points in some space, it groups together points that are closely packed together. ● o(n2 ) 20
  21. 21. Bartoli Luca San Francisco - 15 May 2019 Clustering summary 21 k-means Need to know the number of clusters Hierarchical Clustering o(n3 ) DBSCAN o(n2 )
  22. 22. Bartoli Luca San Francisco - 15 May 2019 DBSCAN improvement DBSCAN (Density-Based Spatial Clustering of Applications with Noise) DBSCAL (Density-Based Spatial Clustering of Applications with LiDAR) 22
  23. 23. Bartoli Luca San Francisco - 15 May 2019 DBSCAL 23 Compute neighbours for each point Calculate the cluster
  24. 24. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 24
  25. 25. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 25
  26. 26. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 26 Neighbors search improvement
  27. 27. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 27
  28. 28. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 28
  29. 29. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 29 radius
  30. 30. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 30 radius
  31. 31. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 31 radius Less search and memory allocation/transfer
  32. 32. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 3. Not check all points 32
  33. 33. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 2. Select MAX 20 nearest points for each 3. Not check all points 33
  34. 34. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 34 LIDAR
  35. 35. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 35 LIDAR
  36. 36. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 36 LIDAR
  37. 37. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 37 LIDAR LIDAR
  38. 38. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 38 LIDAR LIDAR ● Semi-order cloud by horizontal angle
  39. 39. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 39 LIDAR LIDAR ● Semi-order cloud by horizontal angle
  40. 40. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 40 LIDAR LIDAR ● Semi-order cloud by horizontal angle
  41. 41. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 41 LIDAR LIDAR ● Semi-order cloud by horizontal angle
  42. 42. Bartoli Luca San Francisco - 15 May 2019 Calculate the cluster ● Recursive visit for each neighbours points on CPU 42
  43. 43. Bartoli Luca San Francisco - 15 May 2019 Summary 43 Compute neighbours for each point Calculate the cluster 1. Launch a thread for each points (ACC) 2. Select MAX 20 nearest points for each 3. Not check all points 1. Recursive visit for each neighbours points (CPU)
  44. 44. Bartoli Luca San Francisco - 15 May 2019 Timing performance 44 Dataset with 40’000 AVG points for spin
  45. 45. Bartoli Luca San Francisco - 15 May 2019 Tracking 3D 45
  46. 46. Bartoli Luca San Francisco - 15 May 2019 Classification 46
  47. 47. Bartoli Luca San Francisco - 15 May 2019 47 Demo
  48. 48. Bartoli Luca San Francisco - 15 May 2019 48 Luca Bartoli - 228618@studenti.unimore.it Thanks for your attentions https://hipert.unimore.it/
  49. 49. Bartoli Luca San Francisco - 15 May 2019 49
  50. 50. Bartoli Luca San Francisco - 15 May 2019 Environment perception 50 RADAR CAMERAParameters LIDAR RADAR CAMERA Range high medium medium Accuracy high medium low Night time high high low Rain, snow medium high low Distance medium high low Data based on the common sensors present on the market

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