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Occupancy-Grid Mapping with Correlated Range Noise and Moving Clutter

  • Task ID: robotics.occupancy_grid_mapping_correlated_clutter
  • Domain: robotics
  • Subdomain: mapping
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: occupancy_grid, lidar, mapping, correlated_noise, dynamic_clutter, uncertainty

Runtime and requirements

  • Estimated time: 45-90 minutes
  • Python: >=3.9
  • Packages: numpy>=1.24.0
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • poses.npy (data): Robot poses [T, 3] float64 as x, y, yaw in metres/radians.
  • ranges.npy (data): Planar range measurements [T, B] float64 in metres.
  • scan_angles.npy (data): Beam angles [B] float64 relative to the robot heading.
  • metadata.json (data): Grid geometry, max range, cell size, array axis convention, sensor-noise notes, and output shape.

Outputs

  • analysis.py (code): Mapping script that reads data/ and writes the three grid products.
  • occupancy_prob.npy (data): Probability that each grid cell belongs to the static environment.
  • clutter_likelihood.npy (data): Likelihood that a cell is moving/transient clutter rather than static structure.
  • uncertainty_map.npy (data): Mapping uncertainty in [0, 1], high for unobserved or conflicting cells.

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit c9c1ba960854. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.