MAPLE: Mondrian-Anchored Prior for Learning-free Indoor 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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]MAPLE: Mondrian-Anchored Prior for Learning-free Indoor ExplorationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:25 PM
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.
Robots exploring unknown indoor environments must decide where to move while much of the environment remains unseen.
Extractive summary: sentences quoted from the sources.