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 depth images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, seamlessly restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This unique approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for seamless sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. This reconstructed map paves a groundbreaking way for exploring its usability in navigation, localization, map-merging, and other relevant missions. Our proposed approach is rigorously assessed using both a publicly available dataset and field experiments, confirming its efficacy and potential for real-world applications.

翻译：针对资源受限机器人及多机器人系统中高效位置识别的需求，本文提出RecNet——一种同时解决这两大挑战的新方法。RecNet的核心技术流程包括：将三维点云投影为深度图像，通过编码器-解码器框架进行压缩，随后重建距离图像并无缝恢复原始点云。此外，RecNet利用该过程中提取的潜在向量实现高效的位置识别任务。这一独特方法不仅获得了可媲美传统方案的位置识别性能，还保持了紧凑的表示形式，便于机器人间无缝共享以重建集体地图。RecNet的评估涵盖多维指标，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。这种重建地图为探索其在导航、定位、地图融合及其他相关任务中的可用性开辟了创新路径。我们通过公开数据集与实地实验对所提方法进行严格评估，验证了其在现实应用中的有效性与潜力。