Information Gain Is Not All You Need

• URL: http://arxiv.org/abs/2504.01980v3
• Date: Sun, 20 Apr 2025 13:01:02 GMT
• Title: Information Gain Is Not All You Need
• Authors: Ludvig Ericson, José Pedro, Patric Jensfelt,
• Abstract summary: This paper argues that information gain should not serve as an optimization objective in quality-constrained exploration.<n>We propose a novel, distance advantage, which selects frontiers based on a trade-off between proximity to the robot and remoteness from other frontiers.
• Score: 3.053906384469777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous exploration in mobile robotics often involves a trade-off between two objectives: maximizing environmental coverage and minimizing the total path length. In the widely used information gain paradigm, exploration is guided by the expected value of observations. While this approach is effective under budget-constrained settings--where only a limited number of observations can be made--it fails to align with quality-constrained scenarios, in which the robot must fully explore the environment to a desired level of certainty or quality. In such cases, total information gain is effectively fixed, and maximizing it per step can lead to inefficient, greedy behavior and unnecessary backtracking. This paper argues that information gain should not serve as an optimization objective in quality-constrained exploration. Instead, it should be used to filter viable candidate actions. We propose a novel heuristic, distance advantage, which selects candidate frontiers based on a trade-off between proximity to the robot and remoteness from other frontiers. This heuristic aims to reduce future detours by prioritizing exploration of isolated regions before the robot's opportunity to visit them efficiently has passed. We evaluate our method in simulated environments against classical frontier-based exploration and gain-maximizing approaches. Results show that distance advantage significantly reduces total path length across a variety of environments, both with and without access to prior map predictions. Our findings challenge the assumption that more accurate gain estimation improves performance and offer a more suitable alternative for the quality-constrained exploration paradigm.

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