robometric_frame.trajectory_quality.absolute_trajectory_error

Absolute Trajectory Error (ATE) metric for robotics policy trajectory evaluation.

ATE measures the global consistency between predicted and reference trajectories by computing the average point-to-point Euclidean distance.

Reference:

J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Oct. 2012.

F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, and W. Burgard, “An evaluation of the RGB-D SLAM system,” in 2012 IEEE International Conference on Robotics and Automation, IEEE, May 2012.

Classes

AbsoluteTrajectoryError(**kwargs)

Compute Absolute Trajectory Error (ATE) for robotics policy trajectory evaluation.

class robometric_frame.trajectory_quality.absolute_trajectory_error.AbsoluteTrajectoryError(**kwargs)

Compute Absolute Trajectory Error (ATE) for robotics policy trajectory evaluation.

ATE is calculated as:

ATE = (1/L) * Σ(i=1 to L) |p_i - p_i*|_2

where p_i are predicted trajectory points, p_i* are reference (ground truth) trajectory points, and L is the trajectory length. ATE evaluates global consistency by measuring the average Euclidean distance between corresponding points in predicted and reference trajectories.

This metric is critical for navigation and manipulation tasks requiring precise positioning. Lower ATE values indicate better trajectory tracking performance.

This metric accumulates errors across multiple trajectory pairs and returns the average ATE when compute() is called.

Parameters:

**kwargs (Any) – Additional keyword arguments passed to the base Metric class.

Example

>>> from robometric_frame.trajectory_quality import AbsoluteTrajectoryError
>>> import torch
>>> metric = AbsoluteTrajectoryError()
>>> # Perfect prediction (zero error)
>>> predicted = torch.tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])
>>> reference = torch.tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])
>>> metric.update(predicted, reference)
>>> metric.compute()
tensor(0.0000)

Example (with error):

>>> # Prediction with constant offset
>>> metric = AbsoluteTrajectoryError()
>>> predicted = torch.tensor([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]])
>>> reference = torch.tensor([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0]])
>>> metric.update(predicted, reference)
>>> metric.compute()
tensor(1.0000)

Example (batched):

>>> # Batch of trajectory pairs - shape (B, L, D)
>>> metric = AbsoluteTrajectoryError()
>>> predicted_batch = torch.tensor([
...     [[0.0, 0.0], [1.0, 0.0], [2.0, 0.0]],
...     [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]]
... ])
>>> reference_batch = torch.tensor([
...     [[0.0, 0.5], [1.0, 0.5], [2.0, 0.5]],
...     [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0]]
... ])
>>> metric.update(predicted_batch, reference_batch)
>>> result = metric.compute()

Example (3D trajectories):

>>> # 3D trajectory comparison
>>> metric = AbsoluteTrajectoryError()
>>> predicted = torch.tensor([
...     [0.0, 0.0, 0.0],
...     [1.0, 0.0, 0.0],
...     [1.0, 1.0, 0.0]
... ])
>>> reference = torch.tensor([
...     [0.0, 0.0, 0.0],
...     [1.0, 0.0, 0.0],
...     [1.0, 1.0, 1.0]
... ])
>>> metric.update(predicted, reference)
>>> result = metric.compute()

Example (distributed):

>>> # In distributed training, metrics are automatically synced
>>> metric = AbsoluteTrajectoryError()
>>> # On GPU 0
>>> pred_gpu0 = torch.tensor([[0.0, 0.0], [1.0, 0.0]])
>>> ref_gpu0 = torch.tensor([[0.0, 0.0], [1.0, 0.0]])
>>> metric.update(pred_gpu0, ref_gpu0)
>>> # On GPU 1
>>> pred_gpu1 = torch.tensor([[0.0, 0.0], [1.0, 1.0]])
>>> ref_gpu1 = torch.tensor([[0.0, 0.0], [1.0, 0.0]])
>>> metric.update(pred_gpu1, ref_gpu1)
>>> # Final result aggregates across all GPUs
>>> result = metric.compute()

full_state_update: bool = False

total_error: Tensor

num_trajectories: Tensor

__init__(**kwargs)

Initialize the AbsoluteTrajectoryError metric.

update(predicted, reference)

Update metric state with new predicted and reference trajectory pair(s).

Parameters:

• predicted (Tensor) –

  Predicted trajectory tensor of shape (…, L, D) where: - … represents any number of batch dimensions (can be empty) - L is the number of points (must be >= 1) - D is the spatial dimensionality (e.g., 2 for 2D, 3 for 3D)

  Examples of valid shapes: - (L, D): Single trajectory - (B, L, D): Batch of B trajectories - (B, T, L, D): Batch of B sequences with T slices each

  Points should be ordered chronologically along the L dimension.

• reference (Tensor) – Reference (ground truth) trajectory tensor with the same shape as predicted.

Raises:

ValueError – If trajectories have invalid shape, mismatched shapes, or insufficient points.

Return type:

None

compute()

Compute the average Absolute Trajectory Error across all trajectory pairs.

Return type:

Tensor

Returns:

Average ATE as a scalar tensor. Lower values indicate better trajectory tracking performance.

Raises:

RuntimeError – If no trajectories have been recorded.

training: bool