Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud
Abstract
:1. Introduction
- The DBSCAN algorithm with grid for solid state LiDAR effectively saved computational burden.
- The DBSCAN accelerator significantly decreases computation time of clustering.
- The feasibility of the DBSCAN accelerator in autonomous robots was confirmed by connecting it to a low-power core, demonstrating effective object detection with low power consumption.
2. Related Work
2.1. DBSCAN LiDAR Clustering
2.2. DBSCAN Clustering Accelerator
2.3. Low-Power Object Detection
2.4. Object Detection for Autonomous Driving with Various Sensors
3. Architecture
3.1. DBSCAN Clustering System with a Low-Power Core
3.2. DBSCAN Clustering Accelerator
3.2.1. Proposed DBSCAN Algorithm
Algorithm 1 Proposed grid-based DBSCAN algorithm |
Require:
|
Algorithm 2 Clustering(c, , ) |
Require:
|
3.2.2. Architecture
4. Implementation and Analysis
4.1. Environment Setup
4.2. Experiments
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Algorithm | Time Complexity | Features |
---|---|---|---|
KM Kumar et al., 2016 [44] | G-DBSCAN | A novel graph-based index structure method that groups data to accelerate the neighbor search operations. | |
Y Chen et al., 2021 [45] | BLOCK-DBSCAN | ) | Approximation algorithm for large-scale data, judging whether two Inner core blocks are density reachable. |
Y Wang et al., 2020 [29] | Practical Parallel DBSCAN | ) | Grid-based DBSCAN for parallel processing, with tree structure enabling merge groups for a variety # of clusters |
N Ohadi et al., 2020 [41] | SW-DBSCAN | ) | Grid-based DBSCAN and a sliding window approach to large datasets |
N Hanafi et al., 2022 [46] | OP-DBSCAN | Using local samples and concept of operational dataset to find neighbors |
LUTs | Registers | BRAMs | Logic Elements | |
---|---|---|---|---|
Max 10 | - | - | 1,677,321 | 49,760 |
Acc. with Cortex-M0. | 24,806 | 16,170 | 296,996 (18%) | 30,533 (61%) |
Only Acc. | 17,648 | 13,687 | 34,816 (2%) | 22,972 (45%) |
Dataset (a) | Dataset (b) | Dataset (c) | Dataset (d) | Dataset (e) | |
---|---|---|---|---|---|
Time | day | day | night | day | day |
Weather | clouds | clouds | clouds | clouds | rain |
Location | downtown | suburban | boulevard | highway | boulevard |
# of frames | 2929 | 290 | 275 | 190 | 122 |
Platform | Dataset (a) | Dataset (b) | Dataset (c) | Dataset (d) | Dataset (e) | Average |
---|---|---|---|---|---|---|
Original DBSCAN, i7-12700 | 220.35 | 253.8 | 220.4 | 245.8 | 230.07 | 224.48 |
Proposed algorithm, i7-12700 | 7.87 | 14.91 | 8.29 | 7.24 | 7.24 | 8.45 |
Raspberry Pi 4 | 43.87 | 63.85 | 46.27 | 44.74 | 44.74 | 45.69 |
Proposed Accelerator * | 0.89 | 0.88 | 0.89 | 0.87 | 0.9 | 0.886 |
Dataset | Raw Picture | Original DBSCAN SW | Proposed Algorithm SW | Proposed Acc. |
---|---|---|---|---|
Dataset (a), frame 997 | ||||
Dataset (b), frame 24 | ||||
Dataset (c), frame 166 | ||||
Dataset (d), frame 41 | ||||
Dataset (e), frame 37 |
Quantized, i7-12700 | Raspberry Pi 4 | FPGA | |
---|---|---|---|
TDP (W) | 65 | 2.7 | 1.035 |
0.54925 | 0.12336 | 0.00092 |
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Lee, S.; An, S.; Kim, J.; Namkung, H.; Park, J.; Kim, R.; Lee, S.E. Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud. Electronics 2024, 13, 3395. https://doi.org/10.3390/electronics13173395
Lee S, An S, Kim J, Namkung H, Park J, Kim R, Lee SE. Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud. Electronics. 2024; 13(17):3395. https://doi.org/10.3390/electronics13173395
Chicago/Turabian StyleLee, Sangho, Seongmo An, Jinyeol Kim, Hun Namkung, Joungmin Park, Raehyeong Kim, and Seung Eun Lee. 2024. "Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud" Electronics 13, no. 17: 3395. https://doi.org/10.3390/electronics13173395