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parameter_tuning_guide.md

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GenZ-ICP Parameter Tuning Guide

GenZ-ICP is designed to perform well across diverse environments. For optimal performance on each dataset, parameter tuning is recommended. This section provides tips for tuning GenZ-ICP's parameters.

🌟 Key Parameters

voxel_size

: Voxel size of local map (default: 0.3)

  • Larger voxel_size speed up processing but reduce accuracy.
  • It is recommended to adjust voxel_size proportionally to the scale of the environment: larger values for wide outdoor spaces and smaller values for narrow indoor spaces.

max_points_per_voxel

: Maximum points in a single voxel (default: 1)

  • Lower max_points_per_voxel speed up processing but reduce accuracy.
  • Similar to voxel_size, this parameter should also be adjusted proportionally to the scale of the environment.

planarity_threshold

: Threshold for planarity classification (default: 0.2)

  • Lower planarity_threshold classifies planars pair more strictly.
  • In narrow indoor environments, a smaller max_points_per_voxel is typically used, which reduces the number of neighboring points available for covariance calculation during planarity classification. As a result, even planar surfaces can exhibit relatively high local surface variation. To prevent rejecting valid planar pairs due to this, a relatively higher planarity_threshold is recommended for indoor environments.
  • Conversely, in wide outdoor environments, a larger max_points_per_voxel increases the number of neighboring points, resulting in more reliable local surface variation values. Therefore, in outdoor environments, lowering the planarity_threshold is recommended to achieve stricter planarity classification.

desired_num_voxelized_points

: Desired number of points in a voxelized scan (default: 2000)

  • If this value is too large, it can cause CPU overload, while a value too small may lead to inaccurate results.
  • This value should be set proportionally to the scale of the environment: larger values for wide outdoor spaces and smaller values for narrow indoor spaces.
  • Based on this value, the voxel filter size is adaptively adjusted to perform adaptive voxelization.

max_num_iterations

: Maximum number of iterations for the ICP loop (default: 100)

  • Higher max_num_iterations can improve accuracy but increases CPU load.

⚡ Minor Parameters

deskew

: Enables or disables deskewing of LiDAR scans (default: false)

  • When the platform exhibits aggressive motion, enabling deskewing can lead to inaccuracies.
  • Additionally, the effect of deskewing diminishes as the platform's speed decreases.
  • Therefore, for platforms like hand-held devices or quadruped robots that exhibit slow or aggressive motion, setting deskew to false is recommended.
  • Conversely, for platforms with high-speed and smooth motion, such as vehicles used in datasets like KITTI or MulRan, setting deskew to true is recommended.

map_cleanup_radius

: Radius of local map (default: max_range)

  • The default value for map_cleanup_radius is equal to the LiDAR's max_range.
  • In spaces larger than the LiDAR's max_range, where the platform revisits previously visited areas, it is recommended to increase the map_cleanup_radius.
  • However, excessively high values can consume a lot of memory and may lead to inaccurate results. Therefore, it is recommended to keep the value below 300.