Light-Weight Pointcloud Representation with Sparse Gaussian Process
- Resource Type
- Conference
- Authors
- Ali, Mahmoud; Liu, Lantao
- Source
- 2023 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2023 IEEE International Conference on. :4931-4937 May, 2023
- Subject
- Robotics and Control Systems
Solid modeling
Three-dimensional displays
Laser radar
Memory management
Collaboration
Communication channels
Robot sensing systems
- Language
This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication and storage. The proposed approach exploits Sparse Gaussian Process to encode pointcloud into a compact form. Our approach represents both the free space and the occupied space using only one model (one 2D Sparse Gaussian Process) instead of the existing two-model framework (two 3D Gaussian Mixture Models). We achieve this by proposing a variance-based sampling technique that effectively discriminates between the free and occupied space. The new representation requires less memory footprint and can be transmitted across limited-bandwidth communication channels. The framework is extensively evaluated in simulation and it is also demonstrated using a real mobile robot equipped with a 3D LiDAR. Our method results in a 70~100 times reduction in the communication rate compared to sending the raw pointcloud. We have provided a demonstration video 1 1 Video: https://youtu.be/BQZzXiCFGrM and open-sourced our code 2 2 Code: https://github.com/mahmoud-a-ali/vsgp_pcl.