# SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception

arXiv: https://arxiv.org/abs/2510.10506 · HTML: https://mlsanity.com/paper/2510.10506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 12 Oct 2025.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [Deep Non-Line-of-Sight Reconstruction](https://mlsanity.com/paper/2001.09067/) (2020)
- [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://mlsanity.com/paper/2403.10833/) (2024)
- [Towards 3D Vision with Low-Cost Single-Photon Cameras](https://mlsanity.com/paper/2403.17801/) (2024)
- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [Non-line-of-sight tracking of people at long range](https://mlsanity.com/paper/1703.02124/) (2017)
- [Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB](https://mlsanity.com/paper/2411.19474/) (2024)
- [Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR](https://mlsanity.com/paper/2410.03555/) (2024)
- [Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans](https://mlsanity.com/paper/2406.09160/) (2024)
- [Using a Distance Sensor to Detect Deviations in a Planar Surface](https://mlsanity.com/paper/2408.03838/) (2024)
- [Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs](https://mlsanity.com/paper/2508.00169/) (2025)
- [Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment](https://mlsanity.com/paper/2307.07296/) (2023)

## Abstract (arXiv)

Efficient exploration and mapping in unknown indoor environments is a fundamental challenge, with high stakes in time-critical settings. In current systems, robot perception remains confined to line-of-sight; occluded regions remain unknown until physically traversed, leading to inefficient exploration when layouts deviate from prior assumptions. In this work, we bring non-line-of-sight (NLOS) sensing to robotic exploration. We leverage single-photon LiDARs, which capture time-of-flight histograms that encode the presence of hidden objects - allowing robots to look around blind corners. Recent single-photon LiDARs have become practical and portable, enabling deployment beyond controlled lab settings. Prior NLOS works target 3D reconstruction in static, lab-based scenarios, and initial efforts toward NLOS-aided navigation consider simplified geometries. We introduce SuperEx, a framework that integrates NLOS sensing directly into the mapping-exploration loop. SuperEx augments global map prediction with beyond-line-of-sight cues by (i) carving empty NLOS regions from timing histograms and (ii) reconstructing occupied structure via a two-step physics-based and data-driven approach that leverages structural regularities. Evaluations on complex simulated maps and the real-world KTH Floorplan dataset show a 12% gain in mapping accuracy under < 30% coverage and improved exploration efficiency compared to line-of-sight baselines, opening a path to reliable mapping beyond direct visibility.