<p>Today, computer science and robotics have been highly developed.
Simultaneous Localization and Mapping (SLAM) is widely used in mobile robot navigation,
game design, and autonomous vehicles. It can be said that in the future, most
scenarios where mobile robots are applied will require localization and mapping. Among them, the construction of three-dimensional(3D) maps is
particularly important for environment visualization which is the focus of this
research.</p>
<p>In this project, the data used for visualization was collected
using a vision sensor. The data collected by the vision sensor is
processed by ORB-SLAM2 to generate the 3D cloud point maps of the environment. Because, there are a lot of noise in the map points cloud, filters are
used to remove the noise. The generated map points are processed by the
straight-through filter to cut off the points out of the specific range.
Statistical filters are then used to remove sparse outlier noise. Thereafter,
in order to improve the calculation efficiency and retain the necessary terrain
details, a voxel filter is used for downsampling. In order to improve the
composition effect, it is necessary to appropriately increase the sampling
amount to increase surface smoothness. Finally, the processed map points are
visualized using Octomap. The implementation utilizes the services provided by
the Robot Operating System (ROS). The powerful Rviz software on the ROS
platform is used. The processed map points as cloud data are published in ROS
and visualized using Octomap. </p>
<p>Simulation results confirm that Octomap can show the terrain
details well in the 3D visualization of the environment. After the simulations,
visualization experiments for two environments of different complexity are
performed. The experimental results show that the approach can mitigate the
influence of noise on the visualization results to a certain extent. It is
shown that for static high-precision point clouds, Octomap provides a good
visualization. The simulation and experimental results demonstrate the
applicably of the approach to visualize 3D map points for the purpose of
autonomous navigation.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12252977 |
| Date | 07 May 2020 |
| Creators | Benhao Wang (8803199) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/OCTREE_3D_VISUALIZATION_MAPPING_BASED_ON_CAMERA_INFORMATION/12252977 |
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