---

name: odometry description: "Load and validate KITTI odometry benchmark sequences with pykitti.odometry." disable-model-invocation: true metadata: disco-role: operating license: MIT

KITTI odometry sequences

Use this skill for the KITTI odometry benchmark layout: a numbered sequence under sequences/<sequence>, its calibration and timestamps, optional benchmark ground-truth poses, and camera/Velodyne access. The package version covered here is pykitti==0.3.1.

Route and boundary

• Use pykitti.odometry(base_path, sequence, **kwargs) for this layout.
• Use raw-data for date/drive raw data, OXTS packet parsing, or raw-data world poses.
• Use tracking for tracking labels and tracking benchmark files.
• Read the shared troubleshooting guide for package-wide import and installation issues.
• Do not download KITTI archives or launch the plotting demo. The bundled smoke test uses only a temporary local fixture and has no network or GUI behavior.

Detailed contracts are in API reference, dataset layout, workflows, and odometry troubleshooting. Run the safe fixture smoke script after installation from this sub-skill directory.

Quick start

import pykitti

base_path = "/data/kitti/odometry/dataset"
dataset = pykitti.odometry(
    base_path,
    "00",
    frames=[0, 5, 10],       # omit for all positions; use a reusable list/range
    imtype="png",            # default; e.g. "jpg" if files use that extension
)

print(len(dataset), dataset.sequence)
print(dataset.timestamps[0])       # datetime.timedelta
print(dataset.calib.K_cam0)        # 3x3 intrinsic matrix
left = dataset.get_cam0(0)         # PIL.Image, grayscale (mode L)
scan = dataset.get_velo(0)         # float32 array, shape (N, 4)

The constructor eagerly reads calib.txt, times.txt, and the optional pose file. It discovers sensor files with sorted globs; images and scans are loaded only when a property generator or indexed getter is consumed.

Operating checklist

1. Confirm base_path/sequences/<sequence>/calib.txt and times.txt exist.
2. Confirm calibration has numeric P0, P1, P2, P3, and Tr records, each with 12 values. Validate all expected sensor directories before asking for paired streams; the loader permits absent directories.
3. Pass sequence as the directory name (normally a two-character string), and pass frames as valid positional indices. Materialize a subset such as list(range(0, 20, 5)); do not pass a one-shot iterator.
4. Inspect len(dataset), file-list lengths, timestamp types, calibration shapes, and pose availability before indexing.
5. Choose generators for sequential processing (dataset.cam0, dataset.gray, dataset.rgb, dataset.velo) or indexed methods for random access (get_camN, get_gray, get_rgb, get_velo).
6. Keep coordinate-frame names explicit: calibration transforms named T_camN_velo map Velodyne homogeneous points into camera-N coordinates; pose matrices are T_w_cam0.
7. Run the fixture smoke test for a dependency/layout sanity check:

From the odometry sub-skill directory (the directory containing scripts/):

python scripts/odometry_fixture_smoke.py --help
python scripts/odometry_fixture_smoke.py

Validation before real work

len(dataset) is the number of parsed timestamps, not a guarantee that every camera and scan list has that length. Compare len(dataset.timestamps) with len(dataset.camN_files) and len(dataset.velo_files) before synchronized processing. A missing pose file is a supported case: construction prints a warning and leaves dataset.poses == []. A malformed calibration is not silently repaired; stop and fix the source file. See the linked references for failure-specific checks and transform examples.

Expected handoff

A successful odometry load provides a CalibData named tuple, a list of datetime.timedelta timestamps, and optionally a list of 4x4 NumPy poses. Sensor properties return fresh generators, while indexed methods load one item. The loader does not download data, reconcile mismatched stream lengths, or infer missing frame IDs; callers own those checks.