from grid.model.perception.slam.dpv_slam import DPVSLAM
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 = DPVSLAM(use_local = True)
result = model.run(image=img)

The DPVSLAM class implements DPV-SLAM using loop closure for globally consistent trajectory estimation and mapping.

​
class DPVSLAM()
​
use_local
boolean
default:
true

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

​
calib
np.ndarray
default:
"false"

The camera calibration matrix of shape (4,)(4,). Defaults to np.array([320, 320, 320, 240]).

​
def run()
​
rgbimage
np.ndarray
required

The input RGB image of shape (M,N,3)(M,N,3).

​
Returns
np.ndarray

The predicted pose as a 1x6 array.

from grid.model.perception.slam.dpv_slam import DPVSLAM
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 = DPVSLAM(use_local = True)
result = model.run(image=img)

This code is licensed under the MIT License.

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