  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.
