RecNet presents a novel method that transforms 3D point clouds into range images, compresses them using an encoder-decoder framework, and reconstructs the original point cloud. This approach facilitates efficient place recognition tasks and compact representation suitable for sharing among robots. The evaluation of RecNet includes metrics like place recognition performance, structural similarity of reconstructed point clouds, and bandwidth transmission advantages from sharing only latent vectors.
RecNet addresses challenges faced by resource-constrained robots in multi-robot systems by introducing a unique methodology for transforming and compressing 3D point cloud data. The proposed framework not only achieves comparable place recognition results but also reduces computational burden and communication overhead significantly. By focusing on seamless data sharing and promoting multi-robot collaboration, RecNet enhances the capabilities of individual robots while empowering the entire system to build comprehensive maps efficiently.
The network architecture of RecNet consists of two identical encoder legs, a single decoder leg, and a tail network responsible for estimating the similarity between latent bottleneck vectors. The loss functions employed in training include reconstruction loss (Lrec) and place recognition loss (Lpr), with a weighting factor to combine them effectively. Experimental evaluations demonstrate the effectiveness of RecNet in both place recognition performance and point cloud reconstruction tasks.
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by Nikolaos Sta... às arxiv.org 03-06-2024
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