# SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators

arXiv: https://arxiv.org/abs/2503.16000 · HTML: https://mlsanity.com/paper/2503.16000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots](https://mlsanity.com/paper/1903.09845/) (2019)
- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [Generative Adversarial Network based Heuristics for Sampling-based Path Planning](https://mlsanity.com/paper/2012.03490/) (2020)
- [MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches](https://mlsanity.com/paper/2011.00491/) (2020)
- [Rapidly-Exploring Random Graph Next-Best View Exploration for Ground Vehicles](https://mlsanity.com/paper/2108.01012/) (2021)
- [Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping](https://mlsanity.com/paper/2011.02043/) (2020)
- [Map Prediction and Generative Entropy for Multi-Agent Exploration](https://mlsanity.com/paper/2501.13189/) (2025)

## Abstract (arXiv)

We present SenseExpo, a lightweight single-robot exploration framework that integrates a compact map prediction network into a frontier-based strategy. SenseExpo addresses two long-standing challenges in classical methods -- high computational overhead and poor environmental generalization. Our prediction network combines Generative Adversarial Networks (GANs), Transformers, and Fast Fourier Convolution (FFC) to achieve a remarkably small footprint of only 709K parameters. Despite its compactness, SenseExpo outperforms U-Net (24.5M) and LaMa (51M) on the KTH dataset, achieving PSNR 9.026 and SSIM 0.718, representing a 38.7% PSNR gain over LaMa. Cross-domain evaluation further verifies strong generalization with an FID of 161.55 on HouseExpo. In exploration experiments, SenseExpo reaches target coverage 67.9% faster on KTH and 77.1% faster on MRPB~1.0 than a MapEx-style global obstacle-prediction baseline under the same simulator; because the methods predict different map semantics, this comparison evaluates planning utility rather than a direct predictor ranking. Implemented as a plug-and-play ROS (Robot Operating System) node, our framework integrates with existing navigation stacks, providing an efficient solution for resource-constrained robotic systems.