IROS 2025 Workshop
SenseExpo: Lightweight Neural Networks for Efficient Autonomous Exploration and Scene Prediction
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SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
Haohua Que, Haojia Gao, Mingkai Liu, Hoiian Au, Handong Yao, Fei Qiao
Abstract
This work presents SenseExpo, a frontier-based exploration framework powered by a lightweight local map predictor that combines GAN training, a Transformer encoder, and Fast Fourier Convolution. Our smallest model (709k parameters) surpasses much larger baselines (U-Net 24.5M, LaMa 51M) on KTH dataset, achieving PSNR 9.026 and SSIM 0.718, and shows strong cross-domain robustness on HouseExpo (FID 161.55). Leveraging predicted free space for goal selection, SenseExpo accelerates exploration, reducing time by 67.9% on KTH dataset and 77.1% on MRPB 1.0 relative to MapEx, while sustaining high coverage and accuracy. Delivered as a plug-andplay ROS node, it is practical for resource-constrained robots and easy to integrate into existing navigation stacks.
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