SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks

• URL: http://arxiv.org/abs/2503.16000v1
• Date: Thu, 20 Mar 2025 10:07:51 GMT
• Title: SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
• Authors: Haojia Gao, Haohua Que, Hoiian Au, Weihao Shan, Mingkai Liu, Yusen Qin, Lei Mu, Rong Zhao, Xinghua Yang, Qi Wei, Fei Qiao,
• Abstract summary: SenseExpo is an efficient autonomous exploration framework based on a lightweight prediction network.<n>Our smallest model achieves better performance on the KTH dataset than U-net and LaMa.
• Score: 3.6404856388891793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes SenseExpo, an efficient autonomous exploration framework based on a lightweight prediction network, which addresses the limitations of traditional methods in computational overhead and environmental generalization. By integrating Generative Adversarial Networks (GANs), Transformer, and Fast Fourier Convolution (FFC), we designed a lightweight prediction model with merely 709k parameters. Our smallest model achieves better performance on the KTH dataset than U-net (24.5M) and LaMa (51M), delivering PSNR 9.026 and SSIM 0.718, particularly representing a 38.7% PSNR improvement over the 51M-parameter LaMa model. Cross-domain testing demonstrates its strong generalization capability, with an FID score of 161.55 on the HouseExpo dataset, significantly outperforming comparable methods. Regarding exploration efficiency, on the KTH dataset,SenseExpo demonstrates approximately a 67.9% time reduction in exploration time compared to MapEx. On the MRPB 1.0 dataset, SenseExpo achieves 77.1% time reduction roughly compared to MapEx. Deployed as a plug-and-play ROS node, the framework seamlessly integrates with existing navigation systems, providing an efficient solution for resource-constrained devices.

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