The content discusses the development of 4CNet, a model for predicting maps in complex environments with irregular obstacles. It outlines the training process and simulated experiments comparing 4CNet with state-of-the-art methods.
Mobile robots face challenges in unknown cluttered environments due to sensing, energy, and communication limitations. The paper introduces 4CNet as a solution for mobile robot map prediction in such environments. It incorporates various components like conditional consistency models and contrastive learning.
Existing methods mainly use frontier-based exploration but struggle with varying terrain and spatial structures. Robot map prediction can improve exploration efficiency by predicting spatial configurations of unobserved regions. This is crucial for areas that are unreachable or have poor traversability.
The Trajectory Encoder module encodes trajectories from other robots into embedding vectors to extract spatial information. The Map Prediction Network predicts the spatial configuration of unexplored regions based on observed maps and trajectory embeddings. The Confidence Network measures uncertainty to guide exploration effectively.
Training of 4CNet involved CMTP pretraining for Trajectory Encoder, MPN training conditioned on trajectory embeddings, and CN training based on MPN predictions. Simulated experiments compared 4CNet's performance with heuristic and DL methods using metrics like MSE and FSIM.
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arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Aaron Hao Ta... : arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17904.pdfDaha Derin Sorular