RecNet：一种通过距离图像嵌入的可逆点云编码方法，用于多机器人地图共享与重建
RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction
AI总结：
本文提出RecNet，通过将3D点云投影为距离图像并用编码器-解码器压缩，利用潜在向量同时实现高效地点识别和紧凑地图共享，在公开数据集和实地实验中验证了其有效性。
摘要：
在资源受限机器人领域以及多机器人系统中对有效地点识别的需求背景下，本文介绍了RecNet，这是一种同时应对上述两个挑战的新方法。RecNet方法的核心涉及一个变换过程：它将3D点云投影为距离图像，使用编码器-解码器框架对其进行压缩，随后重建距离图像，从而恢复原始点云。此外，RecNet利用从该过程中提取的潜在向量来执行高效的地点识别任务。该方法不仅取得了可比的地点识别结果，还保持了紧凑的表示形式，适合在机器人之间共享以重建它们的集体地图。RecNet的评估涵盖一系列指标，包括地点识别性能、重建点云的结构相似性，以及仅共享潜在向量所带来的带宽传输优势。我们提出的方法使用公开数据集和实地实验$^1$进行了评估，证实了其有效性以及在真实世界应用中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
