Real-Time Compact Environment Representation for UAV Navigation
Abstract
:1. Introduction
- Two novel algorithms, namely the vertical strip extraction algorithm and the plane adjustment algorithm, are proposed to effectively adapt to different obstacle shapes and different surface roughness, as well as to speed up the elimination of irrelevant environmental details by minimizing redundant information.
- The proposed OSAPE modeling scheme, which is the combination of the two proposed algorithms, can convert the normalized data into simplified prisms based on the size of the UAV in real time.
- By building a drone platform with a depth sensor, real-world experiments are conducted to demonstrate the advantage of the proposed scheme over the baseline.
2. Related Works
3. Adaptive Plane Extraction Model
4. The Proposed Vertical Strip Extraction Algorithm
4.1. Statistical Estimation of Obstacles
4.2. Obstacle Identification with a Sliding Window
- The average of the disparity value in the sliding window is within the range and
- more than half of the pixels in the sliding window are within the range .
4.3. Irregular Object Processing
4.4. Vertical Strip Clustering
Algorithm 1 Fast strip clustering algorithm. |
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4.5. Computational Complexity
5. The Proposed Plane Adjustment Algorithm
5.1. Vertical Gap Filling
5.2. Concave Surface Converting
5.3. Adjacent Plane Refinement
- The angle between the two planes is less than the threshold and
- the distance between the boundaries of the adjacent sides is smaller than .
Algorithm 2 Concave surface converting algorithm. |
|
5.4. Computational Complexity
6. Experiment and Analysis
6.1. AirSim Simulation
6.1.1. Compact Model
6.1.2. Memory Usage and Model Precision
6.1.3. Processing Time
6.2. Experiment on the Developed Platform
6.3. Application
7. Conclusions and Future Works
Author Contributions
Funding
Conflicts of Interest
Appendix A. Prove of Lemma 1
References
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Vertical rectangle | |
The parameter of the rectangle | |
, , and | The pitch, roll, and yaw angles |
The normalized disparity data | |
Focal length | |
The Gaussian kernel function | |
The probability density function in column u | |
The minimum threshold of the probability density function | |
The center of the ith peak | |
The width of the ith sliding window | |
The height of the ith sliding window | |
The minimum recognizable obstacle height | |
The minimum height to the passable region for the UAV | |
The minimum width to the passable region for the UAV | |
Estimated disparity value | |
The rth cluster of the vertical strips |
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Meng , K.; Li , D.; He , X.; Liu , M.; Song , W. Real-Time Compact Environment Representation for UAV Navigation. Sensors 2020, 20, 4976. https://doi.org/10.3390/s20174976
Meng K, Li D, He X, Liu M, Song W. Real-Time Compact Environment Representation for UAV Navigation. Sensors. 2020; 20(17):4976. https://doi.org/10.3390/s20174976
Chicago/Turabian StyleMeng , Kaitao, Deshi Li , Xiaofan He , Mingliu Liu , and Weitao Song . 2020. "Real-Time Compact Environment Representation for UAV Navigation" Sensors 20, no. 17: 4976. https://doi.org/10.3390/s20174976
APA StyleMeng , K., Li , D., He , X., Liu , M., & Song , W. (2020). Real-Time Compact Environment Representation for UAV Navigation. Sensors, 20(17), 4976. https://doi.org/10.3390/s20174976