SenseExpo: Lightweight Neural Networks for Efficient Autonomous Exploration and Scene Prediction
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.
BibTeX
@inproceedings{que2025senseexpo,
title = {SenseExpo: Lightweight Neural Networks for Efficient Autonomous Exploration and Scene Prediction},
author = {Que, H. and Gao, H. and Liu, M. and Au, H. and Yao, H. and Qiao, F.},
booktitle = {IROS 2025 Workshop on Edge AI for Robotics: Emerging Technologies and Applications},
year = {2025},
url = {https://openreview.net/forum?id=kElJKr1lKC}
}