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from open_path.tracking.measurement import Device, Measurement
from open_path.tracking.kalman_filter import KalmanFilter
data_location = "data/obj_pose-laser-radar-synthetic-input.txt"
kalman_filter = KalmanFilter()
# each line of data file is either a radar or lidar measurement - feed them into the Kalman filter to update estimate.
with open(data_location, 'r') as f:
for line in f:
line_els = line.strip().split('\t')
# extract measurement
if line_els[0] == 'R':
device, rho, theta, ro_dot, ts, *_ = line_els
measurement = Measurement(Device.radar, [float(rho), float(theta), float(ro_dot)], float(ts))
print(measurement.device,measurement.value, measurement.timestamp)
elif line_els[0] == 'L':
device, x, y, ts, *_ = line_els
measurement = Measurement(Device.lidar, [float(x), float(y)], float(ts))
print(measurement.device,measurement.value, measurement.timestamp)
raise ValueError("Unexpected device type: {}".format(line_els[0]))
# complete a predict and update iteration of Kalman filter
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