Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees
Abstract
:1. Introduction
- We show that the perception data of an autonomous car can be used to replace designated mobile mapping surveys when the geometry of roadside objects, such as trees, is to be measured.
- As a case study, we measure the stem attributes of roadside trees for a city tree register using perception data of an autonomous car system.
- We compare the obtained accuracy against a conventional mobile laser scanning survey for the same trees.
- We discuss the data processing logic needed in general when post-processing perception data of autonomous cars for mapping and surveying applications.
2. Short Review on Using Perception Data of Autonomous Vehicles for Surveying
3. Material and Methods
3.1. Test Area
3.2. Data Acquisition
3.2.1. Autonomous Mapping and Driving Research Platform ARVO
3.2.2. Mobile Mapping System ROAMER
3.3. Data Processing
3.3.1. Post-Processing the Raw Sensor Data of the Autonomous Car System ARVO into a 3D Point Cloud
3.3.2. Post-Processing the Raw Sensor Data of the Mobile Mapping System ROAMER into a 3D Point Cloud
3.3.3. Cropping and Illustrating the Point Cloud Data
3.3.4. Digital Terrain Model Creation
3.3.5. Stem Curve and DBH Estimation
- Find the nearest diameter estimates based on the height z (including the height interval itself), and compute the median (MEDIAN) and median absolute deviation (MAD) of these diameter values. If both of the following inequalities are fulfilled, the point is classified as an outlier
- (a)
- ,
- (b)
- cm.
- If the point is not included in the largest connected set of diameter estimates, it is regarded as an outlier. A set of diameter estimates is regarded as a connected set if all vertical distances between any consecutive diameter estimates sorted based on their z-value satisfy m.
- If the stem curve is estimated from a height interval taller than 3.0 m, the DBH is computed by fitting a line to the lowest 3.0 m of the fitted smoothing spline and then extrapolating the line to m.
- If the stem curve is estimated from a height interval shorter than 3.0 m, a square root function , with h denoting the tree height, is fitted to the stem curve, and then evaluated at m to estimate the DBH.
3.4. Statistical Analysis
4. Results
4.1. Completeness and Correctness Related to Stem Detection
4.2. Accuracy Related to the Measured Stem Attributes
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number of Trees | Road Length (m) | DBH | Stem Curve | |||||
---|---|---|---|---|---|---|---|---|
Mean (cm) | Std. (cm) | Minimum (cm) | Maximum (cm) | Mean (cm) | Minimum Height (m) | Maximum Height (m) | ||
139 | 1310 | 49.3 | 12.5 | 23.0 | 83.8 | 49.2 | 1.0 | 2.9 |
Parameter | ARVO | ROAMER |
---|---|---|
Arc detection | ||
Width of height interval in the z-direction (m) | 0.2 | 0.2 |
Duration of time interval (s) | 0.2 | 0.1 |
Neighborhood radius for DBSCAN (cm) | 7.5 | 7.5 |
Point number threshold for DBSCAN | 4 | 4 |
Outlier threshold in the radial direction for RANSAC (cm) | 3.5 | 3.0 |
Minimum ratio of non-outlying points for RANSAC | 0.75 | 0.75 |
Minimum number of points in arc | 15 | 15 |
Maximum standard deviation of radial residuals in the arc (cm) | 1.75 | 1.75 |
Minimum diameter of the arc (cm) | 10.0 | 10.0 |
Maximum diameter of the arc (cm) | 80.0 | 80.0 |
Minimum central angle of the arc (rad) | ||
Clustering arcs into trees | ||
Neighborhood radius for DBSCAN (cm) | 50 | 50 |
Minimum number of arcs for DBSCAN | 3 | 3 |
Minimum z difference between highest and lowest arc (m) | 1.0 | 1.0 |
Detection of outlying diameter estimates | ||
Number of nearest diameter estimates in the z-direction to use for comparison | 5 | 5 |
Maximum of | 2.0 | 2.0 |
Maximum of (cm) | 4.0 | 4.0 |
Largest allowed z difference between the nearest arcs (m) | 4.0 | 4.0 |
System | Completeness | Correctness | DBH | Stem Curve | ||
---|---|---|---|---|---|---|
Bias | RMSE | Bias | RMSE | |||
ARVO | 96.4% | 87.6% | 2.1 cm (4.3%) | 5.2 cm (10.4%) | 2.3 cm (4.7%) | 4.9 cm (10.2%) |
ROAMER | 100.0% | 84.2% | −2.3 cm (−4.8%) | 7.4 cm (15.0%) | −1.5 cm (−3.1%) | 6.6 cm (14.0%) |
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Hyyppä, E.; Manninen, P.; Maanpää, J.; Taher, J.; Litkey, P.; Hyyti, H.; Kukko, A.; Kaartinen, H.; Ahokas, E.; Yu, X.; et al. Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees. Remote Sens. 2023, 15, 1790. https://doi.org/10.3390/rs15071790
Hyyppä E, Manninen P, Maanpää J, Taher J, Litkey P, Hyyti H, Kukko A, Kaartinen H, Ahokas E, Yu X, et al. Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees. Remote Sensing. 2023; 15(7):1790. https://doi.org/10.3390/rs15071790
Chicago/Turabian StyleHyyppä, Eric, Petri Manninen, Jyri Maanpää, Josef Taher, Paula Litkey, Heikki Hyyti, Antero Kukko, Harri Kaartinen, Eero Ahokas, Xiaowei Yu, and et al. 2023. "Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees" Remote Sensing 15, no. 7: 1790. https://doi.org/10.3390/rs15071790
APA StyleHyyppä, E., Manninen, P., Maanpää, J., Taher, J., Litkey, P., Hyyti, H., Kukko, A., Kaartinen, H., Ahokas, E., Yu, X., Muhojoki, J., Lehtomäki, M., Virtanen, J. -P., & Hyyppä, J. (2023). Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees. Remote Sensing, 15(7), 1790. https://doi.org/10.3390/rs15071790