Optimized DBSCAN clustering for LiDAR Application - talk version

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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.

Automotive

Bartoli Luca
San Francisco - 15 May 2019
DBSCAL Clustering
Bartoli Luca - 228618@studenti.unimore.it
Modena - 15/05/2019
1

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

Bartoli Luca
San Francisco - 15 May 2019
Environment perception
3
LIDAR
RADAR
CAMERA
LiDAR
Radar
Camera

Bartoli Luca
San Francisco - 15 May 2019
Field of view
4
Data based on the common sensors present on the market
LiDAR Radar Camera

Bartoli Luca
San Francisco - 15 May 2019
Distance
5
Data based on the common sensors present on the market
LiDAR Radar Camera

Bartoli Luca
San Francisco - 15 May 2019
Night time
6
Data based on the common sensors present on the market
LiDAR Radar Camera

Bartoli Luca
San Francisco - 15 May 2019
Weather conditions
7
Data based on the common sensors present on the market
LiDAR Radar Camera

Bartoli Luca
San Francisco - 15 May 2019
HiPeRT Prototype
8

Bartoli Luca
San Francisco - 15 May 2019
Real time conditions
9

Bartoli Luca
San Francisco - 15 May 2019
Real time conditions
10
< 40

Bartoli Luca
San Francisco - 15 May 2019
Clustering
11

Bartoli Luca
San Francisco - 15 May 2019
Timing performance
12
Dataset with 40’000
AVG points for spin

Bartoli Luca
San Francisco - 15 May 2019
Timing performance
13
Dataset with 40’000
AVG points for spin
< 40

Bartoli Luca
San Francisco - 15 May 2019
Clustering algorithms
1. K-Means Clustering
2. Hierarchical Clustering
3. DBSCAN
14

Bartoli Luca
San Francisco - 15 May 2019
K-Means Clustering
15
● k-means clustering aims to partition n observations into k clusters.

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!

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

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

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

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
)

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

Bartoli Luca
San Francisco - 15 May 2019
DBSCAL
23
Compute neighbours for each point
Calculate the cluster

Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
24

Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
25

Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
26
Neighbors search improvement

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

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

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

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

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

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

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
34
LIDAR

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
35
LIDAR

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
36
LIDAR

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
37
LIDAR LIDAR

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
38
LIDAR LIDAR
● Semi-order cloud by horizontal angle

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
39
LIDAR LIDAR
● Semi-order cloud by horizontal angle

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
40
LIDAR LIDAR
● Semi-order cloud by horizontal angle

Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
41
LIDAR LIDAR
● Semi-order cloud by horizontal angle

Bartoli Luca
San Francisco - 15 May 2019
Calculate the cluster
● Recursive visit for each neighbours points on CPU
42

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)

Bartoli Luca
San Francisco - 15 May 2019
Timing performance
44
Dataset with 40’000
AVG points for spin

Bartoli Luca
San Francisco - 15 May 2019
Tracking 3D
45

Bartoli Luca
San Francisco - 15 May 2019
Classification
46

Bartoli Luca
San Francisco - 15 May 2019
47
Demo

Bartoli Luca
San Francisco - 15 May 2019
48
Luca Bartoli - 228618@studenti.unimore.it
Thanks for your attentions
https://hipert.unimore.it/

Bartoli Luca
San Francisco - 15 May 2019
49

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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14. Bartoli Luca San Francisco - 15 May 2019 Clustering algorithms 1. K-Means Clustering 2. Hierarchical Clustering 3. DBSCAN 14

15. Bartoli Luca San Francisco - 15 May 2019 K-Means Clustering 15 ● k-means clustering aims to partition n observations into k clusters.

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. 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. 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. 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. 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. 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. 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. Bartoli Luca San Francisco - 15 May 2019 DBSCAL 23 Compute neighbours for each point Calculate the cluster

24. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 24

25. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 25

26. Bartoli Luca San Francisco - 15 May 2019 Compute neighbours 1. Launch a thread for each points 26 Neighbors search improvement

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. 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. 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. 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. 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. 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. 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. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 34 LIDAR

35. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 35 LIDAR

36. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 36 LIDAR

37. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud ● Semi-order cloud by horizontal angle 37 LIDAR LIDAR

38. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 38 LIDAR LIDAR ● Semi-order cloud by horizontal angle

39. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 39 LIDAR LIDAR ● Semi-order cloud by horizontal angle

40. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 40 LIDAR LIDAR ● Semi-order cloud by horizontal angle

41. Bartoli Luca San Francisco - 15 May 2019 Lidar pointcloud 41 LIDAR LIDAR ● Semi-order cloud by horizontal angle

42. Bartoli Luca San Francisco - 15 May 2019 Calculate the cluster ● Recursive visit for each neighbours points on CPU 42

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. Bartoli Luca San Francisco - 15 May 2019 Timing performance 44 Dataset with 40’000 AVG points for spin

45. Bartoli Luca San Francisco - 15 May 2019 Tracking 3D 45

46. Bartoli Luca San Francisco - 15 May 2019 Classification 46

47. Bartoli Luca San Francisco - 15 May 2019 47 Demo

48. Bartoli Luca San Francisco - 15 May 2019 48 Luca Bartoli - 228618@studenti.unimore.it Thanks for your attentions https://hipert.unimore.it/

49. Bartoli Luca San Francisco - 15 May 2019 49

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

Optimized DBSCAN clustering for LiDAR Application - talk version

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Optimized DBSCAN clustering for LiDAR Application - talk version