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.

翻译：在资源受限机器人及多机器人系统中高效位置识别需求的背景下，本文提出RecNet——一种同时应对上述两个挑战的新型方法。RecNet的核心方法涉及一个变换流程：将三维点云投影为距离图像，通过编码器-解码器框架对其进行压缩，随后重建距离图像并恢复原始点云。此外，RecNet利用该流程中提取的潜在向量实现高效的位置识别任务。该方法不仅能取得可比较的位置识别效果，还保持了适用于机器人间共享以重建协同地图的紧凑表示。RecNet的评估涵盖多项指标，包括位置识别性能、重建点云的结构相似度，以及仅共享潜在向量所带来的带宽传输优势。我们通过公开数据集与实地实验对提出的方法进行评估，验证了其在实际应用中的有效性与潜力。