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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

Chinese

探测未知室内环境的机器人必须决定移动的地方,而大部分环境仍然是未见的。预测超出当前传感器范围的未见空间的占用量可以改善这些决定,但最近的方法通常依赖于需要离线训练和探索期间重复推断的学习模型。我们引入了Mondrian-Anchored Prior for Learning-free Indoor Exploration(MAPLE),一个不受培训的预测方法,该方法将Mondrian过程调整为在当前部分地图中观察到的几何线索优先的在线生成条件。MAPLE在探索过程中生成多个可信地图假设,并在各个领域分别评估可见度,以估计边界收益,保持预测可见性差异。通过KTH地板计划和在多个传感器范围内的真实SLAM环境,

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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