Reading the Room: Scale-ambiguous Semantic Registration of Sparse VSLAM Maps to Floor Plans in Partially Built Environments

Key points

• Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan.1
• Our key idea is to define a single robust, differentiable, class-balanced semantic-geometric energy function that governs all stages of the pipeline.1
• We develop two global initialization schemes, leveraging a single inaccurate user-provided correspondence if provided, or estimating the initialization globally using a cross-modal matching network.1
• We evaluate RR on 33 sequences across three active construction sites against classical and learning-based baselines, achieving a 4.5x improvement in mean absolute trajectory error over the strongest baseline in the general unconstrained setting, which increases to 7.6x when an anchor is provided.1

Sources (1)

• [1]Reading the Room: Scale-ambiguous Semantic Registration of Sparse VSLAM Maps to Floor Plans in Partially Built Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:28 PM

  We thus introduce Reading the Room(RR), a global-local approach to register scale-ambiguous, sparse, semantic VSLAM maps to partially built floor plans.

  Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan.

Extractive summary: sentences quoted from the sources.