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Matching

module trajectopy.matching

Trajectopy - Trajectory Evaluation in Python

Gereon Tombrink, 2025 tombrink@igg.uni-bonn.de

function match_trajectories

match_trajectories(
    traj_from: trajectopy.trajectory.Trajectory,
    traj_to: trajectopy.trajectory.Trajectory,
    settings: trajectopy.settings.MatchingSettings = MatchingSettings(method=<MatchingMethod.INTERPOLATION: 'interpolation'>, max_time_diff=0.01, max_distance=0.0, k_nearest=10),
    inplace: bool = True
) → Tuple[trajectopy.trajectory.Trajectory, trajectopy.trajectory.Trajectory]

Matches two trajectories using the specified method

Args:

  • traj_from (Trajectory): Trajectory to match
  • traj_to (Trajectory): Reference trajectory
  • settings (MatchingSettings, optional): Matching settings. Defaults to MatchingSettings().
  • inplace (bool, optional): Whether to modify the input trajectories. Defaults to True.

Supported methods:

  • MatchingMethod.INTERPOLATION: Interpolates the test trajectory onto the reference trajectory using its timestamps
  • MatchingMethod.NEAREST_TEMPORAL: Find the nearest temporal match without interpolation
  • MatchingMethod.NEAREST_SPATIAL: Find the nearest spatial match without interpolation
  • MatchingMethod.NEAREST_SPATIAL_INTERPOLATED: Find the nearest n spatial matches and spatially interpolate using a 3d line

Nearest Spatial

This method matches two trajectories by finding the nearest pose in the target trajectory for each pose in the source trajectory. The distance between two poses is computed using the Euclidean distance between their positions.

Nearest Temporal

This method matches two trajectories using their timestamps by finding the nearest timestamp in the target trajectory for each timestamp in the source trajectory.

Interpolation

This method matches two trajectories by interpolating the timestamps of one trajectory to the timestamps of the other trajectory. The interpolation is linear for both positions and rotations (SLERP).

Nearest Spatial Interpolated

This method matches both trajectories spatially by requesting the nearest k positions from the reference trajectory for each pose in the test trajectory. Then, an interpolation is performed using a 3d line fit of the k nearest positions. After this operation, both trajectories will have the length of the test trajectory. This method does not support rotation matching.

Returns:

  • Tuple[Trajectory, Trajectory]: Matched trajectories

function do_overlap

do_overlap(
    traj_test: trajectopy.trajectory.Trajectory,
    traj_ref: trajectopy.trajectory.Trajectory
) → bool

Checks if two trajectories overlap

Args:

  • traj_test (Trajectory): Test trajectory
  • traj_ref (Trajectory): Reference trajectory

Returns:

  • bool: True if the trajectories overlap, False otherwise

function match_trajectories_interpolation

match_trajectories_interpolation(
    traj_test: trajectopy.trajectory.Trajectory,
    traj_ref: trajectopy.trajectory.Trajectory
) → Tuple[trajectopy.trajectory.Trajectory, trajectopy.trajectory.Trajectory]

Ensures that both trajectories are sampled in the same way

This method will intersect both trajectories with each other and then approximate the trajectory with the higher data rate onto the other trajectory. The sorting and the arc lengths of both trajectories are identical after the call of this method.

Args:

  • traj_test (Trajectory): Test trajectory
  • traj_ref (Trajectory): Reference trajectory

Returns:

  • Tuple[Trajectory, Trajectory]: Both trajectories with the same sampling. The instance which called this method is the first returned trajectory.

function match_trajectories_temporal

match_trajectories_temporal(
    traj_test: trajectopy.trajectory.Trajectory,
    traj_ref: trajectopy.trajectory.Trajectory,
    max_distance: float = 0.01
) → Tuple[trajectopy.trajectory.Trajectory, trajectopy.trajectory.Trajectory]

This method matches both trajectories temporally

After this operation, both trajectories will have the length of the test trajectory. This means, that the reference trajectory may be modified.

Args:

  • traj_test (Trajectory): Test trajectory
  • traj_ref (Trajectory): Reference trajectory
  • max_distance (float, optional): Maximum distance between two timestamps. Defaults to 0.1.

Returns:

  • Tuple[Trajectory, Trajectory]: Matched trajectories

function match_trajectories_spatial

match_trajectories_spatial(
    traj_test: trajectopy.trajectory.Trajectory,
    traj_ref: trajectopy.trajectory.Trajectory,
    max_distance: float = 0.0
) → Tuple[trajectopy.trajectory.Trajectory, trajectopy.trajectory.Trajectory]

This method matches both trajectories spatially

After this operation, both trajectories will have the length of the test trajectory. This means, that the reference trajectory may be modified.

Args:

  • traj_from (Trajectory): Test trajectory
  • traj_to (Trajectory): Reference trajectory
  • max_distance (float, optional): Maximum distance between two poses. Defaults to None. This means all matches are accepted.

Returns:

  • Tuple[Trajectory, Trajectory]: Matched trajectories

function match_trajectories_spatial_interpolation

match_trajectories_spatial_interpolation(
    traj_test: trajectopy.trajectory.Trajectory,
    traj_ref: trajectopy.trajectory.Trajectory,
    max_distance: float = 0.0,
    k_nearest: int = 10
) → Tuple[trajectopy.trajectory.Trajectory, trajectopy.trajectory.Trajectory]

This method matches both trajectories spatially by requesting the nearest two poses from the reference trajectory for each pose in the test trajectory. Then, an interpolation is performed between the two nearest poses.

After this operation, both trajectories will have the length of the test trajectory. This means, that the reference trajectory may be modified.

Args:

  • traj_from (Trajectory): Test trajectory
  • traj_to (Trajectory): Reference trajectory
  • max_distance (float, optional): Maximum distance between two poses. Defaults to None. This means all matches are accepted.
  • k_nearest (int, optional): Number of nearest poses to request from the reference trajectory. Defaults to 10.

Returns:

  • Tuple[Trajectory, Trajectory]: Matched trajectories

function rough_timestamp_matching

rough_timestamp_matching(
    traj_ref: trajectopy.trajectory.Trajectory,
    traj_test: trajectopy.trajectory.Trajectory,
    max_distance: float = 0.0
) → float

This method roughly matches two trajectories temporally

Args:

  • traj_from (Trajectory): Test trajectory
  • traj_to (Trajectory): Reference trajectory

Returns:

  • float: Mean time offset