Point-cloud clustering is a key task in applications like autonomous vehicles and digital twins, where rotating LiDAR sensors commonly generate point-cloud measurements in data streams. The state-ofthe- art algorithms, Lisco (and its parallel equivalent P-Lisco), define a single-pass distance-based clustering. However, while outperforming other batch-based techniques, they cannot incrementally cluster point-clouds from consecutive LiDAR rotations as it cannot exploit result-similarity between rotations. The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose Parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds; and (ii) the fact that pi-Lisco’ parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.

1. Pi-Lisco:
Parallel and Incremental Stream-Based
Point-Cloud Clustering
Hannaneh Najdataei, Vincenzo Gulisano, Philippas Tsigas, Marina
Papatriantafilou
The 37th ACM/SIGAPP Symposium On Applied Computing
April 25 - April 29, 2022, Virtual Conference

2. Motivation
2
Industry 4.

3. 3
Lidar Point Cloud
Lidar Sensor
Particle of Light
distance
360 degrees

4. 4
Lidar Point Cloud Clustering
Lidar Sensor
360 degrees
Raw LiDAR data points
Clustering
Clustered data points
E.g., Density-based,
Distance-based

5. 5
Lidar Point Cloud Clustering Challenges
360
full
rotation
view
One rotation
P points O(P2)
Search for nearest
neighbours
1 [Rusu et al., Semanztic3D2010]
distance < 𝜀
distance >
𝜀
Euclidean-distance based clustering1

6. 6
Lidar Point Cloud Clustering Challenges
360
full
rotation
view
One rotation
P points O(P2)
Search for nearest
neighbours
Disjoint rotations
Report period
Overlapping rotations

7. 7
Lidar Point Cloud Clustering Approaches
Batch Processing
Results
Kd-tree
DBSCAN1
Euclidean Distance-based2
…
1 [Ester et al.,Density-based1996]
2 [Rusu et al., Semantic3D2010]
Continuous Processing
Lisco / pLisco3
Results
𝜀
3 [Najdataei et al.,Continuous2018]

8. 8
Lidar Point Cloud Clustering Approaches
Batch Processing
Results
Kd-tree
DBSCAN1
Euclidean Distance-based2
…
Continuous Processing
Lisco / pLisco3
Results
𝜀
3 [Najdataei et al.,Continuous2018]
1 [Ester et al.,Density-based1996]
2 [Rusu et al., Semantic3D2010]
The Problem: redundant computations
for overlapping windows

9. Incremental Stream-based Results
Pi-Lisco
9
Lidar Point Cloud Clustering Approaches
Batch Processing
Results
Kd-tree
DBSCAN1
Euclidean Distance-based2
…
Continuous Processing
Lisco / pLisco3
Results

10. 10
Data Streaming Requirements
SPE
Operator
(processing unit)
data stream
tuple
• Stateful
• Stateless
E.g., filter, map
E.g., aggregate
Throughput (tuples/sec)
Latency
(time difference between receiving a tuple and producing results)
Sliding
Window

11. S1 … Ss
l1
…
lL
𝜀
Pi-Lisco Overview
Lidar Sensor
360 degrees
Pnew
Reuse information for these parts

12. P
Neighbourhood graph snapshot
before applying an update for P
1
6
5
4
3
2
12
Pi-Lisco Overview
1. Keeping the edge
P
1
6
5
4
3
2
𝜀-neighbours of Pold ={1,2,4}
𝜀-neighbours of Pnew = {4, 6}
3. Removing an old edge
P
1
6
5
4
3
2
2. Adding a new edge
P
1
6
5
4
3
2
Extended neighbourhood of Pold={1,2,3,4}
Lidar points
Indicate 𝜀-neighbours
C1
C2
C3
S1 … Ss
l1
…
lL
𝜀
Pnew

13. P
Neighbourhood graph snapshot
after applying the update of P
1
6
5
4
3
2
13
Pi-Lisco Overview
1. Keeping the edge
P
1
6
5
4
3
2
3. Removing an old edge
P
1
6
5
4
3
2
2. Adding a new edge
P
1
6
5
4
3
2
𝜀-neighbours of Pold = {1, 2, 4} 𝜀-neighbours of Pnew = {4, 6}
C2
C3
C1
P
Neighbourhood graph snapshot
before applying an update for P
1
6
5
4
3
2
C1
C2
C3
S1 … Ss
l1
…
lL
𝜀
Pnew

14. S1 … Ss
l1
…
lL
Pnew
P
Neighbourhood graph snapshot
before applying an update for P
1
6
5
4
3
2
P
Neighbourhood graph snapshot
after applying the update of P
1
6
5
4
3
2
14
Pi-Lisco Key Idea
Extended neighbourhood of Pold = {1, 2, 3, 4}
Extended neighbourhood of Pnew= {2, 3, 4, 6}
𝜀-neighbours of Pold = {1, 2, 4} 𝜀-neighbours of Pnew = {4, 6}
T3
T2
T1 T4
Affected points = {1, 2, 3, 4, 6}
[Gulisano et al.,Scalejoin2016]
C1
C2
C3
C2
C3
C1

15. Adapted Stinger Data Structure
S1 … Ss
l1
…
lL
Pnew
P
Neighbourhood graph snapshot
before applying an update for P
1
6
5
4
3
2
P
Neighbourhood graph snapshot
after applying the update of P
1
6
5
4
3
2
15
Pi-Lisco Key Idea
𝜀-neighbours of Pold = {1, 2, 4} 𝜀-neighbours of Pnew = {4, 6}
T3
T2
T1 T4
C1
C2
C3
C2
C3
C1
[Ediger et al.,Stinger2012]

16. 16
Clustering Implementation in an SPE
Reporti Reporti+1
Reporti-1
rotation i
rotation i+1
time
Lidar Data
Multi Window
Trigger the results and evict the window
Wi
Wi+1
Original Lisco

17. 17
Clustering Implementation in an SPE
Reporti Reporti+1
Reporti-1
rotation i
rotation i+1
time
Lidar Data
Multi Window
Single Window
Trigger the results and evict the window
Wi
Wi+1
W
Trigger the results and slide the window
Original Lisco
Pi-Lisco

18. 18
Evaluation Setup
• Pi-Lisco is implemented in Java and open source https://github.com/dcs-chalmers/pilisco
• Baseline Original Lisco
• Liebre SPE https://github.com/vincenzo-gulisano/Liebre
• Original Lisco as a multi-window aggregate
• Pi-Lisco as a single-window aggregate
• real-world data Velodyne HDL-64E Lidar
• Intel Xeon E5-2695 system (2..10 GHz, 2 sockets with 18 cores each, 64GB RAM)
• 4 performance metrics:
• Incremental processing workload
• Average processing time per tuple
• Sustainable throughput
• Scalability

19. 19
Performance Evaluation
Incremental processing workload
(𝜀 = 0.7 m)
Sustainable throughput
Average processing time per
tuple

20. 20
Performance Evaluation
Scalability

21. Conclusion
hannajd@chalmers.se
Hannaneh Najdataei
21
• Pi-Lisco: Parallel and incremental Stream-based Lidar point-cloud clustering
• Utilises the information from past
• Prevent redundant computations
• Allows parallel processing
• Provides incremental clustering