Hi, I’m using the original NVIDIA Isaac ROS FoundationPose package on a Jetson Orin 64 GB (ROS2 Humble).
I have two questions:
The pose estimation (translation and rotation) is accurate, but the returned pose score is always exactly 0.0.
Here’s an example of the output I’m getting:
vision_msgs.msg.Detection3DArray(
header=std_msgs.msg.Header(
stamp=builtin_interfaces.msg.Time(sec=1752675615, nanosec=287351217),
frame_id='tf_camera'
),
detections=[
vision_msgs.msg.Detection3D(
header=std_msgs.msg.Header(
stamp=builtin_interfaces.msg.Time(sec=1752675615, nanosec=287351217),
frame_id='tf_camera'
),
results=[
vision_msgs.msg.ObjectHypothesisWithPose(
hypothesis=vision_msgs.msg.ObjectHypothesis(
class_id='',
score=0.0
),
pose=geometry_msgs.msg.PoseWithCovariance(
pose=geometry_msgs.msg.Pose(
position=geometry_msgs.msg.Point(
x=-0.12443303316831589,
y=0.09022021293640137,
z=0.7088607549667358
),
orientation=geometry_msgs.msg.Quaternion(
x=0.11492790604863064,
y=-0.02546156634882348,
z=-0.417520445400986,
w=0.9010105230919914
)
),
covariance=array([
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.
])
)
)
],
bbox=vision_msgs.msg.BoundingBox3D(
center=geometry_msgs.msg.Pose(
position=geometry_msgs.msg.Point(
x=-0.12443303316831589,
y=0.09022021293640137,
z=0.7088607549667358
),
orientation=geometry_msgs.msg.Quaternion(
x=0.11492790604863064,
y=-0.02546156634882348,
z=-0.417520445400986,
w=0.9010105230919914
)
),
size=geometry_msgs.msg.Vector3(
x=0.06567460298538208,
y=0.06567499786615372,
z=0.03480000048875809
)
),
id=''
)
]
)
I also noticed the returned message always has an empty class_id (‘’). Could this be the reason why the score is always 0.0?
-
Could you please explain why the final pose score is always zero and how to resolve this?
-
My second question is whether FoundationPose can be used to detect multiple objects, not just one — it seems possible by launching a separate ROS node per object, but this consumes a lot of VRAM.
Thanks!