[Paper Review] Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

Matteo Luperto, Luca Fochetta+1 more|arXiv (Cornell University)|Apr 15, 2020

Robotics and Sensor-Based Localization28 references4 citations

TL;DR

This paper proposes a novel exploration strategy for mobile robots that predicts the layout of partially observed indoor rooms using geometric regularities, enabling faster map building by selecting next-best viewpoints based on predicted visible area and early stopping when no significant new space is expected. The method significantly reduces exploration time, especially in later stages when layout predictions become accurate.

ABSTRACT

We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expected to be discovered. Experimental activities show that our approach is able to effectively predict the layout of partially observed rooms and to use such knowledge to speed up the exploration.

Motivation & Objective

• To improve autonomous robot exploration in unknown indoor environments by leveraging predicted geometric layouts of partially observed rooms.
• To reduce exploration time by using predicted layout to guide viewpoint selection beyond traditional frontier-based methods.
• To enable early termination of exploration when no further meaningful area is expected to be discovered.
• To integrate layout prediction into real-time exploration decisions without prior knowledge of floor plans.
• To validate the method in large-scale simulated and real-world environments with varying levels of observation completeness.

Proposed method

• The method reconstructs the layout of observed areas from a partial grid map using a previously developed layout reconstruction technique that models rooms as polygons and filters out noise.
• It predicts the shape of partially observed rooms by exploiting geometric regularities—such as shared walls and symmetries—common in structured indoor environments like offices and schools.
• The predicted layout is used to estimate the expected visible area (I(p)) from candidate frontier locations, improving upon standard methods that rely only on laser scan footprint.
• A novel early stopping (ES) criterion is introduced, terminating exploration when the predicted unobserved area falls below a threshold (1 m²), based on the layout prediction.
• The approach dynamically updates the layout prediction at each exploration step, refining accuracy as more of the environment is observed.
• The method is evaluated in simulated large-scale indoor environments and validated on a real-world map from prior work, demonstrating robustness to sensor noise and clutter.

Experimental results

Research questions

• RQ1 Can predicting the layout of partially observed rooms improve the efficiency of robot exploration in unknown indoor environments?
• RQ2 How does layout prediction affect the selection of next-best viewpoints compared to conventional frontier-based strategies?
• RQ3 To what extent can early stopping based on predicted layout reduce total exploration time without sacrificing map completeness?
• RQ4 How does the accuracy of layout prediction evolve during exploration, and when does it start to significantly improve performance?
• RQ5 Can the proposed method reliably handle real-world sensor noise and partial observations in practical scenarios?

Key findings

• The proposed method achieves a 50% reduction in exploration time on average when early stopping is triggered at 90% of the environment explored, though one room is occasionally missed.
• At 95% exploration completeness, the early stopping criterion reduces time by 20% on average, successfully completing maps with minor layout inaccuracies.
• The method performs similarly to baseline strategies until approximately 90% of the area is explored, but significantly outperforms them in later stages due to accurate layout predictions.
• The predicted layout reliably estimates the shape of partially observed rooms when 80–90% of the environment is known, enabling effective viewpoint evaluation.
• In real-world maps with sensor clutter, the layout prediction effectively filters noise and provides accurate estimates of unobserved room areas for all frontiers.
• The method demonstrates that integrating layout prediction into exploration decisions leads to measurable performance gains, especially as prediction accuracy increases with environmental observation.