RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction

• URL: http://arxiv.org/abs/2402.02192v2
• Date: Mon, 4 Mar 2024 23:15:14 GMT
• Title: RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction
• Authors: Nikolaos Stathoulopoulos, Mario A.V. Saucedo, Anton Koval and George Nikolakopoulos
• Abstract summary: RecNet is a novel approach to effective place recognition in resource-constrained robots. It projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Our proposed approach is assessed using both a publicly available dataset and field experiments$1$, confirming its efficacy and potential for real-world applications.
• Score: 8.602553195689513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet's methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments$^1$, confirming its efficacy and potential for real-world applications.

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