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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

Korean

현재의 센서 범위를 넘어 보이지 않는 공간의 수용량을 예측하면 이러한 결정을 향상시킬 수 있지만 최근의 접근 방식은 일반적으로 오프라인 훈련과 탐험 중 반복적 인 추론을 필요로하는 학습 모델에 의존합니다. 우리는 학습-무료 실내 탐험을위한 Mondrian-Anchored Prior (MAPLE)를 도입하여 학습-무료 실내 탐험을위한 훈련-무료 예측 접근 방식을 도입하여 Mondrian 프로세스를 현재의 부분 맵에서 관찰 된 지오메트릭 퀴즈를 우선시하는 온라인 생성 조건으로 적응합니다. MAPLE는 여러 가지 유효한 지도를 생성하고 각각의 범위 내에서 분리적으로 시각성을 평가하여 경계 상승을 추

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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