DPVO

The DPVO class is a wrapper for the DPVO model, which estimates camera poses from RGB images using a deep learning approach.

boolean

default:"True"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to True.

np.ndarray

default:"false"

The camera calibration matrix of shape

(4,)

. Defaults to np.array([320, 320, 320, 240]).

This model is currently not available via Cortex.

Uses DPVO to predict the camera pose for the given RGB image relative to the previous one. If this is the first image, initializes the pose estimation routine.

np.ndarray

required

The input RGB image of shape

(M,N,3)

np.ndarray

The predicted pose as a 1x6 tensor containing X, Y, Z positions and R, P, Y orientation.

from grid.model.perception.vo.vo_dpvo import DPVO
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = DPVO(use_local = True)
result = model.run(image=img)

License
Source

This code is licensed under the MIT License.

⌘I

from grid.model.perception.vo.vo_dpvo import DPVO
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = DPVO(use_local = True)
result = model.run(image=img)

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