feat(perception): AprilTag 3D detector with solvePnP TF transforms#2092
feat(perception): AprilTag 3D detector with solvePnP TF transforms#2092Null-Phnix wants to merge 8 commits into
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Confidence Score: 4/5The AprilTag detection code itself is ready to merge, but all_blueprints.py carries unrelated deletions of working module entries that should be separated out first. The three new AprilTag files are clean and all previously flagged problems have been fixed. The only finding is in all_blueprints.py, where 19 entries for existing, still-present modules are deleted without explanation — any dimos run invocation or module-by-name lookup that used those keys (e.g. teleop-quest-go2, relocalization-module, keyboard-teleop-a750) will silently break. dimos/robot/all_blueprints.py — the unrelated blueprint/module removals need to be verified as intentional or reverted before merging. Important Files Changed
Sequence DiagramsequenceDiagram
participant Camera
participant AprilTagDetectionModule
participant AprilTagDetector
participant LCMTF
Camera->>AprilTagDetectionModule: color_image (Image)
Camera->>AprilTagDetectionModule: camera_info (CameraInfo)
Note over AprilTagDetectionModule: camera_info cached via subscribe callback
AprilTagDetectionModule->>AprilTagDetectionModule: sharpness_barrier + backpressure
AprilTagDetectionModule->>AprilTagDetector: detect(image, camera_info)
AprilTagDetector->>AprilTagDetector: ArucoDetector.detectMarkers(grey)
AprilTagDetector->>AprilTagDetector: solvePnP → rvec, tvec
AprilTagDetector->>AprilTagDetector: Rodrigues → R_mat → Quaternion
AprilTagDetector-->>AprilTagDetectionModule: list[AprilTagDetection]
AprilTagDetectionModule->>LCMTF: "tf.publish(Transform: camera_optical → marker/{family}_{id})"
AprilTagDetectionModule-->>AprilTagDetectionModule: annotated_image.publish(Image)
AprilTagDetectionModule-->>AprilTagDetectionModule: tags_json.publish(str)
Reviews (9): Last reviewed commit: "chore: remove dead code — unused module ..." | Re-trigger Greptile |
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| camera_info = self.camera_info.get_next() | ||
| if camera_info is None: | ||
| logger.debug("AprilTag module: no camera_info available yet") | ||
| return |
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get_next() blocks and never returns None
get_next() either returns a value or raises an Exception (after a 10-second timeout) — it never returns None. The if camera_info is None guard on line 110 is therefore dead code. In practice, every frame processed before the first camera_info arrives will block the reactive worker thread for 10 seconds, then the raised exception will propagate through the subscribe callback and silently kill the subscription. The correct pattern used elsewhere (e.g. ObjectTracking.start()) is to subscribe to camera_info as a separate stream, store the latest value in an instance attribute with a lock, and check if self._latest_camera_info is None instead.
| from dimos.msgs.geometry_msgs.Pose import Pose | ||
| from dimos.msgs.geometry_msgs.PoseStamped import PoseStamped |
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Pose
Pose is imported but never referenced in this file. Only PoseStamped is used.
| from dimos.msgs.geometry_msgs.Pose import Pose | |
| from dimos.msgs.geometry_msgs.PoseStamped import PoseStamped | |
| from dimos.msgs.geometry_msgs.PoseStamped import PoseStamped |
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| dict_id = _FAMILIES[family] | ||
| self._dictionary = cv2.aruco.getPredefinedDictionary(dict_id) | ||
| self._params = detector_params or _DEFAULT_DETECTOR_PARAMS |
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DetectorParameters singleton
_DEFAULT_DETECTOR_PARAMS is instantiated once at module load time and shared across every AprilTagDetector that does not supply a custom detector_params. cv2.aruco.DetectorParameters is a mutable object; if any caller mutates the shared instance, all detectors sharing it will be affected. Creating a fresh instance per detector avoids this class of subtle bug.
| self._params = detector_params or _DEFAULT_DETECTOR_PARAMS | |
| self._params = detector_params if detector_params is not None else cv2.aruco.DetectorParameters() |
| corners = det.corners_image.astype(np.int32) | ||
| cv2.polylines(cv_image, [corners], True, (0, 255, 0), 2) |
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cv2.polylines corner array shape
cv2.polylines expects each contour to have shape (N, 1, 2). Passing a (4, 2) array works in some OpenCV versions but raises an error in others. Reshaping is the safe, portable approach.
| corners = det.corners_image.astype(np.int32) | |
| cv2.polylines(cv_image, [corners], True, (0, 255, 0), 2) | |
| corners = det.corners_image.astype(np.int32).reshape(-1, 1, 2) | |
| cv2.polylines(cv_image, [corners], True, (0, 255, 0), 2) |
| dict_id = _FAMILIES[family] | ||
| self._dictionary = cv2.aruco.getPredefinedDictionary(family_dict_cv[family]) |
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family_dict_cv is never defined anywhere in this file — the correct mapping is _FAMILIES, which is declared at module scope. This NameError will crash every call to AprilTagDetector(...) before the object is constructed. Additionally, dict_id computed on the previous line is immediately orphaned.
| dict_id = _FAMILIES[family] | |
| self._dictionary = cv2.aruco.getPredefinedDictionary(family_dict_cv[family]) | |
| self._dictionary = cv2.aruco.getPredefinedDictionary(_FAMILIES[family]) |
| self._params: cv2.aruco.DetectorParameters = cv2.aruco.DetectorParameters() | ||
| self._refinement: cv2.aruco.RefineParameters = cv2.aruco.RefineParameters() |
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detector_params argument is accepted in the public __init__ signature but is always discarded — a fresh cv2.aruco.DetectorParameters() is created unconditionally. Any caller who passes a custom DetectorParameters (e.g. to tune corner-refinement or thresholding) will silently get the default parameters instead.
| self._params: cv2.aruco.DetectorParameters = cv2.aruco.DetectorParameters() | |
| self._refinement: cv2.aruco.RefineParameters = cv2.aruco.RefineParameters() | |
| self._params: cv2.aruco.DetectorParameters = ( | |
| detector_params if detector_params is not None else cv2.aruco.DetectorParameters() | |
| ) | |
| self._refinement: cv2.aruco.RefineParameters = cv2.aruco.RefineParameters() |
| @pytest.mark.parametrize("depth", [0.5, 1.0, 2.0, 3.0]) | ||
| def test_depth_scale(self, detector_small: AprilTagDetector, camera_info: CameraInfo, depth: float) -> None: | ||
| """Estimated Z should scale approximately linearly with true depth.""" | ||
| img = _synthetic_tag_image( | ||
| tag_id=99, | ||
| tag_size_m=0.08, | ||
| depth_m=depth, | ||
| tag_shift_px=(0, 0), | ||
| ) | ||
| results = detector_small.detect(img, camera_info) | ||
| assert len(results) == 1 | ||
| z = results[0].pose.position.z | ||
| # Tolerate 15 % error due to synthetic / ideal nature. | ||
| assert abs(z - depth) < 0.15 * depth, f"depth {depth} got Z={z}" |
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test_depth_scale will fail at depth=3.0 m due to pixel-size clamping
In _synthetic_tag_image, apparent_size_px = int(500 * 0.08 / 3.0) = 13, which is less than 20 and gets clamped to 20. A 20 px marker with tag_size_m=0.08 causes solvePnP to recover a depth of fx * tag_size_m / pixel_width ≈ 500 * 0.08 / 20 = 2.0 m, but the assertion demands |z - 3.0| < 0.45 m. The 1.0 m error guarantees a failure at this parametrize value. The fix is to skip the depth-accuracy assertion (or the whole iteration) whenever clamping has occurred — the clamping only ensures the marker is detectable; it intentionally breaks the geometric depth correspondence.
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- Add AprilTagDetector class using OpenCV ArUco + solvePnP for 6-DOF marker pose
- Add AprilTagDetectionModule dimos Module with In[Image]/In[CameraInfo] -> TF
- Publishes camera_optical -> marker/{family}_{tag_id} transforms
- Emits annotated images and JSON-serialised detections
- Full synthetic test suite with depth and rotation validation
- Configurable family, tag_size_m, max_freq
Closes dimensionalOS#2036
… ts=0 edge-case in tests
…op 3m synthetic depth test
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Null-Phnix
requested review from
leshy,
mustafab0,
paul-nechifor and
spomichter
as code owners
1 participant
Summary
Implements the AprilTag/ArUco fiducial marker 3D detector requested in #2036.
Consumes
color_image+camera_info, detects markers via OpenCV ArUco, andestimates 6-DOF pose with
solvePnPusing the full camera intrinsics anddistortion model. Publishes TF transforms so downstream modules (navigation,
manipulation, multi-robot identification) can use detected tags as landmarks.
What changed
dimos/perception/detection/detectors/apriltag.pyAprilTagDetector— reusable detector class.tag36h11,aruco_4x4_50, …).cv2.aruco.ArucoDetector+solvePnP(SOLVEPNP_ITERATIVE).CameraInfo→cameraMatrix+distCoeffsconversion.AprilTagDetectiondataclass with pose, corners, and canonicalmarker/{family}_{tag_id}frame ID.dimos/perception/detection/apriltag_detector.pyAprilTagDetectionModule— full DimOSModulewith typed streams.In[Image]/In[CameraInfo]inputs.tf(Transform) for each detected tag.annotated_image(Out[Image]) with overlays.tags_json(Out[str]) with serialised pose + corners.sharpness_barrier+backpressurereactive pipeline.family,tag_size_m,max_freq.deploy()pattern used byDetection2DModule.dimos/perception/detection/detectors/test_apriltag.pyface-on tags via quaternion conversion.
Architecture notes
detectors/base.pyABC, separatedetector class from Module wrapper,
In/Outtyped streams via autoconnect.Detection2DModule.track()pattern: computescamera_optical → marker/...transforms directly from solvePnPtvec/R.dimos[apriltag]already pullsopencv-contrib-python.How to test (replay / sim)
1. Synthetic unit tests
2. Deploy in a blueprint
3. Inspect TF output
Known limitations / future work
solvePnPpose is in camera_optical frame. Converting toworldrequires the caller to have the current
base_link → camera_opticalTF available (standard in all blueprints that have a camera).
is a natural next extension.
extreme angles (> 70°) or heavy motion blur.
Verification
py_compilepasses on all new files.dimos[apriltag].Closes #2036