SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
This provides an efficient solution for autonomous exploration on resource-constrained devices, though it appears incremental as it builds on existing neural network components.
The paper tackles the problem of computational overhead and poor generalization in autonomous exploration by proposing SenseExpo, a framework using a lightweight prediction network with 709k parameters, which achieves a 38.7% PSNR improvement over a 51M-parameter model and reduces exploration time by up to 77.1% compared to baseline methods.
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.