<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the 3d dense map construction system of indoor scene by mobile robot, the existing single sensor method cannot improve the positioning accuracy and reconstruction accuracy of robot, as well as the requirement of rapidity. Therefore, it is applied to THE ORB-SLAM with three parallel threads of track tracking, map reconstruction and loopback detection. Through depth camera pose to splice point cloud of building three-dimensional dense point cloud, in the 3 d reconstruction, a computer can not rely on GPU parallel computing, using only the CPU recovery environment three-dimensional dense scene map method, further reduce the time of the map construction, improve the efficiency of the reconstruction, thus improve the overall performance. Since only the ORB features were retained in the map during the construction of ORB-SLAM2, sparse point cloud map was established. Fortunately, the framework structure of ORB-SlAM2 was relatively clear. Only one thread needed to be added for the maintenance of point cloud map, and the key frames generated by ORB-SLAM2 were passed into the point cloud map construction thread. Use incoming keyframes to generate a map with dense point clouds.</p>
</abstract>

In the 3d dense map construction system of indoor scene by mobile robot, the existing single sensor method cannot improve the positioning accuracy and reconstruction accuracy of robot, as well as the requirement of rapidity. Therefore, it is applied to THE ORB-SLAM with three parallel threads of track tracking, map reconstruction and loopback detection. Through depth camera pose to splice point cloud of building three-dimensional dense point cloud, in the 3 d reconstruction, a computer can not rely on GPU parallel computing, using only the CPU recovery environment three-dimensional dense scene map method, further reduce the time of the map construction, improve the efficiency of the reconstruction, thus improve the overall performance. Since only the ORB features were retained in the map during the construction of ORB-SLAM2, sparse point cloud map was established. Fortunately, the framework structure of ORB-SlAM2 was relatively clear. Only one thread needed to be added for the maintenance of point cloud map, and the key frames generated by ORB-SLAM2 were passed into the point cloud map construction thread. Use incoming keyframes to generate a map with dense point clouds.

3D Reconstruction System Based on Multi Sensor

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"3D Reconstruction System Based on Multi Sensor"}

In the 3d dense map construction system of indoor scene by mobile robot, the existing single sensor method cannot improve the positioning accuracy and...

3D Reconstruction System Based on Multi Sensor

Data publikacji: 26 maj 2023

Zakres stron: 58 - 66

DOI: https://doi.org/10.2478/ijanmc-2022-0016

Słowa kluczowe
Mobilerobot, Three-Dimensional Reconstruction, The ORB-SLAM, Depth Sensor, Inertial Sensor

© 2022 Fan Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

3D Reconstruction System Based on Multi Sensor

Data publikacji: 26 maj 2023

Zakres stron: 58 - 66

DOI: https://doi.org/10.2478/ijanmc-2022-0016

Słowa kluczoweMobilerobot, Three-Dimensional Reconstruction, The ORB-SLAM, Depth Sensor, Inertial Sensor

© 2022 Fan Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Mobilerobot, Three-Dimensional Reconstruction, The ORB-SLAM, Depth Sensor, Inertial Sensor