An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames
Abstract
:1. Introduction
2. Related Works
2.1. Mobile LiDAR Systems
2.2. Point Cloud Data Merging
3. Entropy Analysis based Window Size Optimization Scheme
3.1. Application Scenario
3.2. System Modeling
3.3. Window Size Optimization Algorithm
Algorithm 1 Finding Optimal Window Size | |
Input: I, TI, R, N | |
Output: Optimal window size ω | |
1 | Calculate ER(X) |
2 | Vmin = INF |
3 | for i = 2 to 10 do |
4 | Ei(X) = 0 |
5 | for j = 1 to TI do |
6 | Ei(X) += |
7 | end for |
8 | if < Vmin then |
9 | Vmin |
10 | ω = i |
11 | end if |
12 | end for |
13 | return ω |
4. Evaluation Results
4.1. Experimental Environment
4.2. Effects of the Proposed Window Mechanism
4.3. Entropy Indicator Comparison
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Velodyne Puck | Velodyne Ultra Puck | |
---|---|---|
# of channels | 16 | 32 |
Max range | 100 m | 200 m |
Accuracy | ±3 cm | ±3 cm |
FoV | 30° (−15° to +15°) | 40° (−25° to +15°) |
Rotation rate | 5~20 Hz | 5~20 Hz |
Vertical angular resolution | 2° | 0.33° |
Horizontal angular resolution | 0.1~0.4° | 0.1~0.4° |
# of frames | 10 | 10 |
Weight | 830 g | 925 g |
Notation | Description |
---|---|
E(X) | Indicator of system X’s entropy |
I | Set of point of interests |
TI | Total number of point of interests |
R | Set of referenced linear structures’ ID |
W | Window size (2 <= W <= 10) |
Total number of extracted linear structure at each point of interest in ideal result (e.g., N = 16 in Figure 5a) | |
The number of detected linear structure at point i (pi) | |
The number of incorrectly detected linear structure at point i (pi) | |
The probability of correctly detected linear structure at point i (pi) | |
ER(X) | Entropy indicator of ideal result |
EW(X) | Entropy indicator of actual result at window size W |
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Share and Cite
Kim, T.; Jung, J.; Min, H.; Jung, Y.-H. An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames. Sensors 2022, 22, 9293. https://doi.org/10.3390/s22239293
Kim T, Jung J, Min H, Jung Y-H. An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames. Sensors. 2022; 22(23):9293. https://doi.org/10.3390/s22239293
Chicago/Turabian StyleKim, Taesik, **man Jung, Hong Min, and Young-Hoon Jung. 2022. "An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames" Sensors 22, no. 23: 9293. https://doi.org/10.3390/s22239293