local-costmap-config

What problem does it solve? Setting up the Nav2 local costmap incorrectly causes unstable controller behavior, wasted CPU, and slow obstacle reactions. This Skill provides the correct parameter structure and sizing rules so the rolling window costmap works reliably with controllers like DWB, RPP, and MPPI. ## Core Features & Use Cases - Correct Frame and Window Setup: Enforces rolling_window true and global_frame odom to avoid TF jitter from localization corrections. - Sizing and CPU Guidance: Provides a cell-count formula and sizing table so the costmap matches robot speed without overloading the update loop. - Multi-Sensor Layer Configuration: Supplies a complete YAML example combining voxel_layer, obstacle_layer, range_sensor_layer, and inflation_layer with proper combination methods. - Use Case: A robotics engineer tuning a differential-drive indoor robot uses this Skill to set a 5m x 5m rolling costmap at 0.05m resolution fed by a LaserScan, avoiding the common mistake of using the map frame. ## Quick Start Ask the assistant to generate a Nav2 local costmap YAML configuration for your robot's sensors and size constraints.