Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations
Abstract
:1. Introduction
1.1. Background
1.2. Previous Studies
1.3. Present Work
2. Sensor Constellation of MMS
3. Registration of Mobile Point Clouds and Panoramic Images Based on Sensor Constellations
3.1. Flowchart of Proposed Method
3.2. Segmentation Feature Point Extraction Based on Sensor Constellation
3.3. Division of Original Points into Blocks
3.3.1. Division of the Point Clouds Captured by LS-1
3.3.2. Division of the Point Clouds Captured by LS-2 and LS-3
3.4. Registration of a Block’s Point Clouds and Panoramic Images
4. Case Studies
4.1. Case Area
4.2. Registration Results
4.2.1. Efficiency Evaluation for Different Laser Scanner’s Point Clouds
4.2.2. Visualization of Registration Results
4.3. Accuracy Evaluation
4.3.1. Evaluation Method
4.3.2. Evaluation Result
4.4. Discussion of the Main Factors Influence Registration Accuracy
4.4.1. Time Synchronization for Different Sensors
4.4.2. Vehicle Speed
4.4.3. Positioning Error
5. Discussion and Conclusions
Acknowledgements
Author Contributions
Conflicts of Interest
Appendix A
ID | Before Registration | After Registration | |||||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | X | Y | Z | ||||
1 | 557,266.54 | 3,464,931.96 | 5.95 | 557,266.54 | 3,464,931.96 | 5.95 | 0.00 | 0.00 | 0.00 |
2 | 558,161.27 | 3,465,074.76 | 5.89 | 558,161.30 | 3,465,074.78 | 5.89 | 0.04 | 0.00 | 0.04 |
3 | 558,049.90 | 3,465,035.68 | 5.88 | 558,049.81 | 3,465,035.66 | 5.91 | 0.09 | 0.03 | 0.10 |
4 | 557,132.70 | 3,466,020.56 | 5.73 | 557,132.70 | 3,466,020.46 | 5.74 | 0.10 | 0.01 | 0.10 |
5 | 557,113.87 | 3,466,025.27 | 5.75 | 557,113.87 | 3,466,025.37 | 5.75 | 0.10 | 0.00 | 0.10 |
6 | 55,581.99 | 3,465,065.84 | 5.82 | 555,811.08 | 3,465,065.73 | 5.82 | 0.14 | 0.00 | 0.14 |
7 | 556,456.29 | 3,465,032.10 | 7.29 | 556,456.19 | 3,465,032.16 | 7.30 | 0.12 | 0.01 | 0.12 |
8 | 556,885.17 | 3,465,062.65 | 10.97 | 556,885.06 | 3,465,062.53 | 10.96 | 0.16 | 0.01 | 0.16 |
9 | 556,547.91 | 3,465,037.44 | 6.99 | 556,547.81 | 3,465,037.5 | 7.00 | 0.12 | 0.01 | 0.12 |
10 | 557,127.50 | 3,466,267.06 | 5.86 | 557,127.36 | 3,466,266.96 | 5.84 | 0.17 | 0.02 | 0.17 |
11 | 557,108.58 | 3,465,007.55 | 5.47 | 557,108.63 | 3,465,007.78 | 5.46 | 0.24 | 0.01 | 0.24 |
12 | 558,050.04 | 3,465,035.63 | 5.99 | 558,049.75 | 3,465,035.67 | 5.98 | 0.29 | 0.01 | 0.29 |
13 | 557,130.75 | 3,464,592.06 | 5.92 | 557,130.82 | 3,464,592.06 | 5.92 | 0.07 | 0.00 | 0.07 |
14 | 557,131.73 | 3,465,560.56 | 7.81 | 557,131.71 | 3,465,560.38 | 7.84 | 0.18 | 0.03 | 0.18 |
15 | 557,118.36 | 3,465,905.69 | 5.88 | 557,118.45 | 3,465,905.49 | 5.88 | 0.22 | 0.00 | 0.22 |
16 | 557,144.34 | 3,464,662.67 | 5.66 | 557,144.32 | 3,464,662.33 | 5.66 | 0.34 | 0.00 | 0.34 |
17 | 556,322.90 | 3,465,038.68 | 7.58 | 556,322.63 | 3,465,038.75 | 7.59 | 0.28 | 0.01 | 0.28 |
18 | 558,458.61 | 3,465,031.80 | 5.81 | 558,458.40 | 3,465,031.94 | 5.80 | 0.25 | 0.01 | 0.25 |
19 | 557,388.51 | 3,465,059.40 | 9.48 | 557,388.86 | 3,465,059.36 | 9.47 | 0.35 | 0.01 | 0.35 |
20 | 555,258.91 | 3,465,052.20 | 7.46 | 555,259.19 | 3,465,052.06 | 7.46 | 0.31 | 0.00 | 0.31 |
ID | Before Registration | After Registration | |||||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | X | Y | Z | ||||
1 | 529,034.53 | 3,426,244.34 | 7.09 | 529,034.53 | 3,426,244.34 | 7.09 | 0.00 | 0.00 | 0.00 |
2 | 529,278.04 | 3,424,996.51 | 13.86 | 529,278.04 | 3,424,996.51 | 13.86 | 0.00 | 0.00 | 0.00 |
3 | 529,296.45 | 3,423,696.15 | 6.23 | 529,296.45 | 3,423,696.15 | 6.23 | 0.00 | 0.00 | 0.00 |
4 | 529,601.83 | 3,422,438.62 | 6.41 | 529,601.90 | 3,422,438.66 | 6.42 | 0.08 | 0.01 | 0.08 |
5 | 530,364.20 | 3,421,574.78 | 6.68 | 530,364.23 | 3,421,574.81 | 6.69 | 0.04 | 0.01 | 0.04 |
6 | 531,210.65 | 3,420,911.27 | 7.35 | 531,210.69 | 3,420,911.32 | 7.34 | 0.06 | 0.01 | 0.06 |
7 | 531,896.87 | 3,420,146.05 | 5.94 | 531,896.92 | 3,420,146.07 | 5.94 | 0.05 | 0.00 | 0.05 |
8 | 532,229.98 | 3,419,233.14 | 5.86 | 532,230.08 | 3,419,233.16 | 5.86 | 0.10 | 0.00 | 0.10 |
9 | 532,474.61 | 3,418,398.48 | 7.56 | 532,474.69 | 3,418,398.40 | 7.55 | 0.11 | 0.01 | 0.11 |
10 | 532,079.20 | 3,417,664.18 | 6.25 | 532,079.23 | 3,417,664.06 | 6.25 | 0.12 | 0.00 | 0.12 |
11 | 532,531.10 | 3,417,673.95 | 7.63 | 532,530.99 | 3,417,673.98 | 7.64 | 0.11 | 0.01 | 0.11 |
12 | 532,504.34 | 3,418,387.89 | 7.60 | 532,504.34 | 3,418,387.89 | 7.60 | 0.00 | 0.00 | 0.00 |
13 | 532,251.38 | 3,419,256.93 | 6.05 | 532,251.36 | 3,419,256.79 | 6.06 | 0.14 | 0.01 | 0.14 |
14 | 531,904.05 | 3,420,172.37 | 6.27 | 531,903.94 | 3,420,172.31 | 6.27 | 0.13 | 0.00 | 0.13 |
15 | 531,224.95 | 3,420,924.95 | 7.56 | 531,224.96 | 3,420,924.79 | 7.57 | 0.16 | 0.01 | 0.16 |
16 | 530,387.18 | 3,421,581.64 | 6.96 | 530,387.20 | 3,421,581.51 | 6.97 | 0.13 | 0.01 | 0.13 |
17 | 529,614.89 | 3,422,455.49 | 6.17 | 529,614.77 | 3,422,455.43 | 6.18 | 0.13 | 0.01 | 0.13 |
18 | 529,316.68 | 3,423,691.46 | 6.09 | 529,316.59 | 3,423,691.46 | 6.09 | 0.09 | 0.00 | 0.09 |
19 | 529,301.14 | 3,424,989.35 | 13.86 | 529,301.14 | 3,424,989.35 | 13.86 | 0.00 | 0.00 | 0.00 |
20 | 529,073.28 | 3,426,233.00 | 7.77 | 529,073.23 | 3,426,233.09 | 7.77 | 0.10 | 0.00 | 0.10 |
ID | Before Registration | After Registration | |||||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | X | Y | Z | ||||
1 | 554,018.04 | 3,464,962.70 | −5.59 | 554,018.04 | 3,464,962.67 | −5.59 | 0.03 | 0.00 | 0.03 |
2 | 553,953.54 | 3,464,949.68 | −8.90 | 553,953.55 | 3,464,949.61 | −8.90 | 0.07 | 0.00 | 0.07 |
3 | 553,871.04 | 3,464,933.35 | −13.28 | 553,870.95 | 3,464,933.31 | −13.26 | 0.10 | 0.02 | 0.10 |
4 | 553,805.49 | 3,464,922.17 | −16.53 | 553,805.40 | 3,464,922.12 | −16.52 | 0.10 | 0.01 | 0.10 |
5 | 553,722.65 | 3,464,911.07 | −20.63 | 553,722.65 | 3,464,911.11 | −20.63 | 0.04 | 0.00 | 0.04 |
6 | 553,663.13 | 3,464,905.16 | −23.48 | 553,663.32 | 3,464,905.15 | −23.45 | 0.19 | 0.03 | 0.19 |
7 | 553,612.16 | 3,464,901.45 | −25.88 | 553,612.26 | 3,464,901.40 | −25.87 | 0.11 | 0.01 | 0.11 |
8 | 553,551.50 | 3,464,898.54 | −28.75 | 553,551.71 | 3,464,898.52 | −28.73 | 0.21 | 0.02 | 0.21 |
9 | 553,491.84 | 3,464,897.35 | −31.51 | 553,491.74 | 3,464,897.36 | −31.50 | 0.10 | 0.01 | 0.10 |
10 | 553,342.61 | 3,464,901.97 | −38.04 | 553,342.71 | 3,464,901.89 | −38.03 | 0.13 | 0.01 | 0.13 |
11 | 553,273.86 | 3,464,907.56 | −40.30 | 553,273.76 | 3,464,907.57 | −40.31 | 0.10 | 0.01 | 0.10 |
12 | 553,169.33 | 3,464,920.51 | −40.67 | 553,169.19 | 3,464,920.33 | −40.67 | 0.23 | 0.00 | 0.23 |
13 | 553,080.47 | 3,464,935.61 | −38.84 | 553,080.35 | 3,464,935.52 | −38.85 | 0.15 | 0.01 | 0.15 |
14 | 553,016.10 | 3,464,948.96 | −37.44 | 553,016.16 | 3,464,948.87 | −37.42 | 0.11 | 0.02 | 0.11 |
15 | 552,928.97 | 3,464,970.59 | −35.29 | 552,928.79 | 3,464,970.54 | −35.28 | 0.19 | 0.01 | 0.19 |
16 | 552,833.60 | 3,464,998.49 | −31.51 | 552,833.80 | 3,464,998.53 | −31.50 | 0.20 | 0.01 | 0.20 |
17 | 552,686.29 | 3,465,051.83 | −24.51 | 552,686.17 | 3,465,051.78 | −24.51 | 0.13 | 0.00 | 0.13 |
18 | 552,514.30 | 3,465,128.75 | −15.75 | 552,514.17 | 3,465,128.70 | −15.76 | 0.14 | 0.01 | 0.14 |
19 | 552,454.70 | 3,465,156.64 | −12.71 | 552,454.52 | 3,465,156.65 | −12.73 | 0.18 | 0.02 | 0.18 |
20 | 552,290.84 | 3,465,229.46 | −4.36 | 552,290.65 | 3,465,229.47 | −4.38 | 0.19 | 0.02 | 0.19 |
ID | Before Registration | After Registration | |||||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | X | Y | Z | ||||
1 | 551,179.32 | 3,451,668.88 | 5.57 | 551,179.28 | 3,451,668.87 | 5.57 | 0.04 | 0.00 | 0.04 |
2 | 551,244.46 | 3,451,317.43 | 5.56 | 551,244.43 | 3,451,317.42 | 5.57 | 0.03 | 0.01 | 0.03 |
3 | 551,341.51 | 3,451,259.73 | 6.26 | 551,341.46 | 3,451,259.65 | 6.26 | 0.09 | 0.00 | 0.09 |
4 | 551,487.13 | 3,450,511.96 | 7.77 | 551,487.12 | 3,450,512.06 | 7.79 | 0.10 | 0.02 | 0.10 |
5 | 551,361.83 | 3,450,949.48 | 7.66 | 551,361.84 | 3,450,949.40 | 7.68 | 0.08 | 0.02 | 0.08 |
6 | 551,013.69 | 3,451,205.67 | 5.76 | 551,013.70 | 3,451,205.64 | 5.75 | 0.03 | 0.01 | 0.03 |
7 | 551,253.57 | 3,451,315.07 | 5.58 | 551,253.54 | 3,451,315.06 | 5.59 | 0.03 | 0.01 | 0.03 |
8 | 551,165.41 | 3,451,722.61 | 5.81 | 551,165.39 | 3,451,722.44 | 5.83 | 0.17 | 0.02 | 0.17 |
9 | 551,458.43 | 3,450,603.59 | 10.52 | 551,458.47 | 3,450,603.60 | 10.52 | 0.04 | 0.00 | 0.04 |
10 | 551,532.58 | 3,450,377.08 | 6.07 | 551,532.62 | 3,450,377.11 | 6.06 | 0.05 | 0.01 | 0.05 |
11 | 551,308.99 | 3,451,118.60 | 5.32 | 551,308.93 | 3,451,118.58 | 5.32 | 0.06 | 0.00 | 0.06 |
12 | 551,197.67 | 3,451,603.26 | 5.49 | 551,197.76 | 3,451,603.20 | 5.47 | 0.11 | 0.02 | 0.11 |
13 | 551,114.85 | 3,451,986.20 | 5.57 | 551,114.90 | 3,451,986.13 | 5.58 | 0.09 | 0.01 | 0.09 |
14 | 551,043.35 | 3,452,331.86 | 5.55 | 551,043.40 | 3,452,331.59 | 5.55 | 0.27 | 0.00 | 0.27 |
15 | 550,908.49 | 3,452,987.78 | 7.50 | 550,908.52 | 3,452,987.57 | 7.50 | 0.21 | 0.00 | 0.21 |
16 | 550,873.00 | 3,453,263.91 | 12.26 | 550,872.98 | 3,453,264.28 | 12.27 | 0.37 | 0.01 | 0.37 |
17 | 551,009.54 | 3,452,439.48 | 5.86 | 551,009.49 | 3,452,439.87 | 5.89 | 0.39 | 0.03 | 0.39 |
18 | 551,132.00 | 3,451,866.43 | 5.82 | 551,132.06 | 3,451,866.35 | 5.85 | 0.10 | 0.03 | 0.10 |
19 | 551,101.99 | 3,452,047.10 | 5.51 | 551,102.07 | 3,452,046.83 | 5.52 | 0.28 | 0.01 | 0.28 |
20 | 550,984.51 | 3,452,623.22 | 6.30 | 550,984.55 | 3,452,623.00 | 6.30 | 0.22 | 0.00 | 0.22 |
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Laser Scanner | Lens of Panoramic Camera |
---|---|
LS-1 | Lens-0 |
LS-2 | Lens-1, Lens-2, Lens-5 |
LS-3 | Lens-3, Lens-4, Lens-5 |
Case Type | Overpass | Freeway | Tunnel | Surface Roads |
---|---|---|---|---|
Environment complexity | Complex | Simple | Simple | Complex |
GPS signal | Good | Good | None | Average |
Length (km) | 30.8 | 27.5 | 2.0 | 11.6 |
Average speed (km/h) | 30 | 40 | 30 | 22 |
Time span (min) | 61.50 | 41.27 | 3.34 | 31.75 |
Type | Laser Scanner | Total Points | Matched Points | Match Rate (%) | Computation Time (s) |
---|---|---|---|---|---|
Overpass | LS-1 | 120,210,560 | 120,099,095 | 99.91 | 3163 |
LS-2 | 46,040,486 | 45,750,431 | 99.37 | 1972 | |
LS-3 | 48,009,700 | 47,625,623 | 99.20 | 2060 | |
Freeway | LS-1 | 72,612,193 | 72,601,383 | 99.98 | 1910 |
LS-2 | 26,218,503 | 26,207,957 | 99.95 | 1092 | |
LS-3 | 28,495,605 | 28,486,135 | 99.96 | 1187 | |
Surface roads | LS-1 | 61,756,177 | 61,695,284 | 99.90 | 1625 |
LS-2 | 23,249,154 | 23,238,371 | 99.95 | 968 | |
LS-3 | 26,393,008 | 26,381,647 | 99.95 | 1099 | |
Tunnel | LS-1 | 8,582,302 | 8,511,419 | 99.79 | 199 |
LS-2 | 4,797,171 | 4,787,142 | 99.17 | 225 | |
LS-3 | 6,002,796 | 5,971,450 | 99.47 | 250 |
Laser Scanner | Total Points | Matched Point | Total Computation Time (s) | Average Computation Efficiency |
---|---|---|---|---|
LS-1 | 263,161,232 | 262,907,161 | 6897 | 38,155 |
LS-2 | 101,305,314 | 99,983,901 | 4257 | 23,870 |
LS-3 | 108,901,109 | 108,464,855 | 4596 | 24,006 |
Index | Case Area | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Min. | Max. | Avg. | Min. | Max. | Avg. | Min. | Max. | Avg. | ||
1 | Overpass | 0.000 | 0.340 | 0.178 | 0.000 | 0.030 | 0.009 | 0.000 | 0.352 | 0.179 |
2 | Intersection | 0.031 | 0.393 | 0.139 | 0.000 | 0.020 | 0.011 | 0.033 | 0.394 | 0.140 |
3 | Tunnel | 0.030 | 0.228 | 0.135 | 0.000 | 0.020 | 0.011 | 0.030 | 0.228 | 0.135 |
4 | Freeway | 0.000 | 0.160 | 0.079 | 0.000 | 0.010 | 0.020 | 0.000 | 0.161 | 0.112 |
Sensor | Time System |
---|---|
GPS | GPS time |
IMU | GPS time |
Panoramic camera | GPS time |
Laser scanner | Windows time |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yao, L.; Wu, H.; Li, Y.; Meng, B.; Qian, J.; Liu, C.; Fan, H. Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations. Sensors 2017, 17, 837. https://doi.org/10.3390/s17040837
Yao L, Wu H, Li Y, Meng B, Qian J, Liu C, Fan H. Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations. Sensors. 2017; 17(4):837. https://doi.org/10.3390/s17040837
Chicago/Turabian StyleYao, Lianbi, Hangbin Wu, Yayun Li, Bin Meng, Jinfei Qian, Chun Liu, and Hongchao Fan. 2017. "Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations" Sensors 17, no. 4: 837. https://doi.org/10.3390/s17040837
APA StyleYao, L., Wu, H., Li, Y., Meng, B., Qian, J., Liu, C., & Fan, H. (2017). Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations. Sensors, 17(4), 837. https://doi.org/10.3390/s17040837