Semantic-Aware Predictive Mapping for Exploration and Navigation

Key points

• This work investigates whether semantic door cues improve predictive geometric occupancy mapping around such ambiguous regions.1
• We modify a subset of the CogniPlan dataset by inserting door-induced ambiguities into partial occupancy maps while keeping the ground-truth layouts unchanged.1
• We compare a geometry-only control model with a semantic-cued model trained on the same modified dataset, where the semantic-cued model receives an additional door channel.1
• These results suggest that semantic cues can improve predictive occupancy completion in regions where geometric observations alone are ambiguous.1

Sources (1)

• [1]Semantic-Aware Predictive Mapping for Exploration and Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:42 PM

  Predictive mapping can support robotic exploration and navigation by estimating unseen geometric layouts from partial occupancy observations.

  This work investigates whether semantic door cues improve predictive geometric occupancy mapping around such ambiguous regions.

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