# MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions

arXiv: https://arxiv.org/abs/2409.15590 · HTML: https://mlsanity.com/paper/2409.15590/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting](https://mlsanity.com/paper/1805.03356/) (2018)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments](https://mlsanity.com/paper/2204.03140/) (2022)
- [Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans](https://mlsanity.com/paper/2406.09160/) (2024)
- [Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles](https://mlsanity.com/paper/2309.06986/) (2023)
- [Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data](https://mlsanity.com/paper/2407.08726/) (2024)
- [Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition](https://mlsanity.com/paper/2307.15202/) (2023)
- [UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments](https://mlsanity.com/paper/2407.08720/) (2024)
- [Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping](https://mlsanity.com/paper/2109.08490/) (2021)
- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping](https://mlsanity.com/paper/2403.11876/) (2024)

## Built on by (most cited first, 12 of 12)

- [Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments](https://mlsanity.com/paper/2602.21967/) (2026)
- [OpenFrontier: General Navigation with Visual-Language Grounded Frontiers](https://mlsanity.com/paper/2603.05377/) (2026)
- [Efficient Navigation in Unknown Indoor Environments with Vision-Language Models](https://mlsanity.com/paper/2510.04991/) (2025)
- [MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning](https://mlsanity.com/paper/2503.01548/) (2025)
- [HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward](https://mlsanity.com/paper/2510.15679/) (2025)
- [SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators](https://mlsanity.com/paper/2503.16000/) (2025)
- [Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration](https://mlsanity.com/paper/2504.04061/) (2025)
- [SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception](https://mlsanity.com/paper/2510.10506/) (2025)
- [ClutterNav: Gradient-Guided Search for Efficient 3D Clutter Removal with Learned Costmaps](https://mlsanity.com/paper/2511.12479/) (2025)
- [TF-Lane: Traffic Flow Module for Robust Lane Perception](https://mlsanity.com/paper/2602.01277/) (2026)
- [FlatLands: Generative Floormap Completion From a Single Egocentric View](https://mlsanity.com/paper/2603.16016/) (2026)
- [Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration](https://mlsanity.com/paper/2605.22814/) (2026)

## Abstract (arXiv)

Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing approaches, such as conventional frontier approaches, have difficulty leveraging the predictability and explore with simple heuristics such as `closest first'. Recent works use deep learning techniques to predict unknown regions of the map, using these predictions for information gain calculation. However, these approaches are often sensitive to the predicted map quality or do not reason over sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12.4% improvement than representative map-prediction based exploration and 25.4% improvement than nearest frontier approach. Website: mapex-explorer.github.io