RecNet: 距離画像埋め込みによる可逆的点群符号化とマルチロボット地図共有・再構成
RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction
3D点群を距離画像に変換して圧縮・再構成し、その潜在ベクトルを場所認識とロボット間の地図共有に活用する手法を提案した論文。
著者: Nikolaos Stathoulopoulos, Mario A. V. Saucedo, Anton Koval, George Nikolakopoulos
分類: cs.RO, cs.CV
原文アブストラクト
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.
