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MAPLE: Mondrian-Anchored Prior for Learning-free Indoor Exploration

Kelvin Aladum, Nisar R. Ahmed · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28566 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

現行センサー範囲を超える見えない空間の占有を予測することは、これらの決定を改善することができるが、最近のアプローチは、通常、オフライン訓練と探査中に繰り返される推測を必要とする学習モデルに依存する。我々は、モンドリアン・アンコード・プリオール(MAPLE)を導入し、学習フリー・インド・エクスカレーション(Learning-free Indoor Exploration)のためのモンドリアン・プリオール(MAPLE)は、訓練フリーの予測アプローチで、モンドリアン・プロセスをオンライン生成条件に適応させ、現在の部分地図で観察された地理的ヒューズに優先する。MAPLEは、複数の合理的な地図仮説を

Machine translation (Meta M2M100); the English original follows.

Abstract

Robots exploring unknown indoor environments must decide where to move while much of the environment remains unseen. Predicting the occupancy of unseen space beyond the current sensor range can improve these decisions, but recent approaches commonly rely on learned models that require offline training and repeated inference during exploration. We introduce the Mondrian-Anchored Prior for Learning-free Indoor Exploration (MAPLE), a training-free predictive approach that adapts the Mondrian process into an online generative prior conditioned on observed geometric cues in the current partial map. MAPLE generates multiple plausible map hypotheses and evaluates visibility separately within each to estimate frontier gain, preserving differences in predicted visibility across hypotheses. Across KTH floor-plans and real-SLAM environments at multiple sensor ranges, MAPLE consistently improves over non-predictive exploration and remains competitive with learned predictive approaches. Its lightweight procedural construction enables predictive exploration with limited computational resources. Experimental results further show that more accurate map prediction does not necessarily produce better exploration, highlighting the importance of how predictions are represented and used in exploration decisions.

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Topics: Computer science, Artificial intelligence, Visibility, Machine learning, Process (computing), Inference

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