We designed and implemented an adaptive point cloud streaming framework for immersive teleoperation that can be used, f.i., in remote inspection and maintenance of buildings and civil structures (bridges, monuments).
The system includes a distance-based sampling strategy that dynamically adapts the point cloud bitrate to the estimated time-varying network bandwidth, addressing constraints imposed by limited computational resources on both the robot and the HMD. The framework is implemented on a real mobile robot and evaluated under various network conditions, including a public 5G connection, demonstrating its effectiveness and robustness in supporting immersive remote teleoperation. Code is available at our GitHub repository (https://github.com/Diane-Spirit).
This work received two awards: