TY - GEN
T1 - Building 3D Map Based on Monte Carlo Localization and Feature Extraction
AU - Fan, Zhun
AU - Chen, Ying
AU - Li, Chong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/10
Y1 - 2020/10/10
N2 - In this project, we mainly explore and construct the 3D indoor map. The goal of this paper is to merge the data from the laser and kinect sensor with Monte Carlo Location(MCL) in 2D map. We use the laser range sensor to construct the 2D map and get the robot's pose transformation matrix. Then, we obtain the color and depth image by using the kinect sensor and build 3D point cloud map by using the feature extraction method, getting the kinect's pose transformation matrix. After that, we get the optimal pose transformation by using the Kalman Filter to calibrate the robot's pose transformation matrix and the kinect's pose transformation matrix. Finally, the optimal pose transformation matrix is employed to accomplish the local 3D map and construct the global 3D indoor map. To show the superiority of our method, we make some experiments and compare with some other algorithms. Experimental results show that our method has a better superiority.
AB - In this project, we mainly explore and construct the 3D indoor map. The goal of this paper is to merge the data from the laser and kinect sensor with Monte Carlo Location(MCL) in 2D map. We use the laser range sensor to construct the 2D map and get the robot's pose transformation matrix. Then, we obtain the color and depth image by using the kinect sensor and build 3D point cloud map by using the feature extraction method, getting the kinect's pose transformation matrix. After that, we get the optimal pose transformation by using the Kalman Filter to calibrate the robot's pose transformation matrix and the kinect's pose transformation matrix. Finally, the optimal pose transformation matrix is employed to accomplish the local 3D map and construct the global 3D indoor map. To show the superiority of our method, we make some experiments and compare with some other algorithms. Experimental results show that our method has a better superiority.
UR - http://www.scopus.com/inward/record.url?scp=85099028677&partnerID=8YFLogxK
U2 - 10.1109/CYBER50695.2020.9279172
DO - 10.1109/CYBER50695.2020.9279172
M3 - Conference Proceeding
AN - SCOPUS:85099028677
T3 - 10th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2020
SP - 99
EP - 104
BT - 10th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2020
Y2 - 10 October 2020 through 13 October 2020
ER -