Real-Time Efficient Environment Compression and Sharing for Multi-Robot Cooperative Systems

Efficient environment sharing is crucial for multi-robot tasks, such as exploration and navigation. However, real-time environment sharing faces significant challenges due to limited communication bandwidth. Inspired by image JPEG compression, this paper presents a novel solution for efficient compression and real-time sharing of environmental point clouds. The framework directly maps the 3D point cloud obtained by the sensor into a panorama, implicitly reducing data dimensions. An event-trigger mechanism, based on the visibility of the point cloud, selectively merges consecutive frames of the point cloud into same panorama, thereby avoiding the transmission of redundant data. To reduce the proportion of invalid data in the panorama, a Mixed Integer Programming (MIP) problem is formulated to extract valuable segments from the panorama before compression. The point cloud is then compressed in the frequency domain, achieving high compression performance and decompression accuracy. The lightweight architecture without GPU acceleration makes the framework easily deployable and suitable for real-time environment sharing in multi-robot systems. Simulations and real-world experiments in various scenarios validate the effectiveness of the proposed method.