PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration

2025-03-10

AI Summary

In autonomous exploration of unknown indoor environments, path-level information gain estimation often suffers from overestimation and high computational overhead. To address this, we propose an active perception planning method that tightly couples path integral modeling with lightweight semantic map prediction. Our key contribution is the first joint optimization of sensor coverage path integrals and real-time map prediction, augmented by an Expected Observation Mask (EOM) approximation mechanism for efficient, low-bias information gain estimation. Evaluated within a floorplan-driven real-world scenario validation framework, our approach achieves 23% higher exploration efficiency, 18% improved mapping completeness, and a 67% reduction in planning latency over state-of-the-art methods on standard benchmarks—effectively mitigating both the overestimation bias and computational bottlenecks inherent in conventional path integral formulations.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration.

Problem

• Estimating information gain for autonomous robot exploration
• Overcoming computational challenges in pathwise information gain
• Integrating predictive mapping to improve exploration efficiency

Innovation

• Pathwise integration for comprehensive information gain estimation
• Map prediction to mitigate overestimation in exploration
• Efficient computation of expected observation mask along paths