outlier rejection in separate fn and other readability changes
akrishnan86
parent
634682738e
commit
a490017669
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@ -15,7 +15,7 @@ Date: September 2020
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"""
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from collections import defaultdict
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from typing import Tuple, List
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from typing import List, Tuple
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import numpy as np
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@ -28,59 +28,48 @@ OUTLIER_WEIGHT_THRESHOLD = 0.1
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def get_data() -> Tuple[gtsam.Values, List[gtsam.BinaryMeasurementUnit3]]:
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""""Returns global rotations and unit translation directions between 8 cameras
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that lie on a circle and face the center. The poses of 8 cameras are obtained from SFMdata
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and the unit translations directions between some camera pairs are computed from their
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""""Returns global rotations and unit translation directions between 8 cameras
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that lie on a circle and face the center. The poses of 8 cameras are obtained from SFMdata
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and the unit translations directions between some camera pairs are computed from their
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global translations. """
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# Using toy dataset in SfMdata for example.
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wTc = SFMdata.createPoses(gtsam.Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0))
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wTc_list = SFMdata.createPoses(gtsam.Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0))
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# Rotations of the cameras in the world frame.
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wRc_values = gtsam.Values()
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# Normalized translation directions from camera i to camera j
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# in the coordinate frame of camera i.
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i_iZj_list = []
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for i in range(0, len(wTc) - 2):
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for i in range(0, len(wTc_list) - 2):
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# Add the rotation.
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wRc_values.insert(i, wTc[i].rotation())
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wRi = wTc_list[i].rotation()
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wRc_values.insert(i, wRi)
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# Create unit translation measurements with next two poses.
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for j in range(i + 1, i + 3):
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i_iZj = gtsam.Unit3(wTc[i].rotation().unrotate(
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wTc[j].translation() - wTc[i].translation()))
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# Compute the translation from pose i to pose j, in the world reference frame.
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w_itj = wTc_list[j].translation() - wTc_list[i].translation()
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# Obtain the translation in the camera i's reference frame.
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i_itj = wRi.unrotate(w_itj)
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# Compute the normalized unit translation direction.
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i_iZj = gtsam.Unit3(i_itj)
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i_iZj_list.append(gtsam.BinaryMeasurementUnit3(
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i, j, i_iZj, gtsam.noiseModel.Isotropic.Sigma(3, 0.01)))
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# Add the last two rotations.
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wRc_values.insert(len(wTc) - 1, wTc[-1].rotation())
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wRc_values.insert(len(wTc) - 2, wTc[-2].rotation())
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return (wRc_values, i_iZj_list)
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wRc_values.insert(len(wTc_list) - 1, wTc_list[-1].rotation())
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wRc_values.insert(len(wTc_list) - 2, wTc_list[-2].rotation())
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return wRc_values, i_iZj_list
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def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3,
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wRc_values: gtsam.Values) -> gtsam.Values:
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"""Estimate poses given rotations and normalized translation directions between cameras.
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def prune_to_inliers(w_iZj_list: gtsam.BinaryMeasurementsUnit3) -> gtsam.BinaryMeasurementsUnit3:
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"""Removes outliers from a list of Unit3 measurements that are the
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translation directions from camera i to camera j in the world frame."""
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Args:
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iZj_list -- List of normalized translation direction measurements between camera pairs,
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Z here refers to measurements. The measurements are of camera j with reference
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to camera i (iZj), in camera i's coordinate frame (i_). iZj represents a unit
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vector to j in i's frame and is not a transformation.
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wRc_values -- Rotations of the cameras in the world frame.
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# Indices of measurements that are to be used as projection directions.
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# These are randomly chosen. All sampled directions must be unique.
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num_directions_to_sample = min(
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MAX_1DSFM_PROJECTION_DIRECTIONS, len(w_iZj_list))
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sampled_indices = np.random.choice(
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len(w_iZj_list), num_directions_to_sample, replace=False)
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Returns:
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Values -- Estimated poses.
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"""
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# Convert the translation direction measurements to world frame using the rotations.
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w_iZj_list = gtsam.BinaryMeasurementsUnit3()
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for i_iZj in i_iZj_list:
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w_iZj = gtsam.Unit3(wRc_values.atRot3(i_iZj.key1())
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.rotate(i_iZj.measured().point3()))
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w_iZj_list.append(gtsam.BinaryMeasurementUnit3(
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i_iZj.key1(), i_iZj.key2(), w_iZj, i_iZj.noiseModel()))
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# Indices of measurements that are to be used as projection directions.
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# These are randomly chosen.
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sampled_indices = np.random.choice(len(w_iZj_list), min(
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MAX_1DSFM_PROJECTION_DIRECTIONS, len(w_iZj_list)), replace=False)
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# Sample projection directions from the measurements.
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projection_directions = [w_iZj_list[idx].measured()
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for idx in sampled_indices]
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@ -91,8 +80,8 @@ def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3,
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algorithm = gtsam.MFAS(w_iZj_list, direction)
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outlier_weights.append(algorithm.computeOutlierWeights())
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# Compute average of outlier weights. Each outlier weight is a map from a pair of Keys
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# (camera IDs) to a weight, where weights are proportional to the probability of the edge
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# Compute average of outlier weights. Each outlier weight is a map from a pair of Keys
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# (camera IDs) to a weight, where weights are proportional to the probability of the edge
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# being an outlier.
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avg_outlier_weights = defaultdict(float)
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for outlier_weight_dict in outlier_weights:
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@ -101,8 +90,37 @@ def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3,
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# Remove w_relative_tranlsations that have weight greater than threshold, these are outliers.
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w_iZj_inliers = gtsam.BinaryMeasurementsUnit3()
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[w_iZj_inliers.append(Z) for Z in w_iZj_list
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if avg_outlier_weights[(Z.key1(), Z.key2())] < OUTLIER_WEIGHT_THRESHOLD]
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[w_iZj_inliers.append(Z) for Z in w_iZj_list if avg_outlier_weights[(
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Z.key1(), Z.key2())] < OUTLIER_WEIGHT_THRESHOLD]
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return w_iZj_inliers
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def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3,
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wRc_values: gtsam.Values) -> gtsam.Values:
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"""Estimate poses given rotations and normalized translation directions between cameras.
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Args:
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i_iZj_list: List of normalized translation direction measurements between camera pairs,
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Z here refers to measurements. The measurements are of camera j with reference
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to camera i (iZj), in camera i's coordinate frame (i_). iZj represents a unit
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vector to j in i's frame and is not a transformation.
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wRc_values: Rotations of the cameras in the world frame.
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Returns:
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Values: Estimated poses.
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"""
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# Convert the translation direction measurements to world frame using the rotations.
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w_iZj_list = gtsam.BinaryMeasurementsUnit3()
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for i_iZj in i_iZj_list:
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w_iZj = gtsam.Unit3(wRc_values.atRot3(i_iZj.key1())
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.rotate(i_iZj.measured().point3()))
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w_iZj_list.append(gtsam.BinaryMeasurementUnit3(
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i_iZj.key1(), i_iZj.key2(), w_iZj, i_iZj.noiseModel()))
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# Remove the outliers in the unit translation directions.
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w_iZj_inliers = prune_to_inliers(w_iZj_list)
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# Run the optimizer to obtain translations for normalized directions.
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wtc_values = gtsam.TranslationRecovery(w_iZj_inliers).run()
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@ -115,8 +133,8 @@ def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3,
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def main():
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wRc_values, w_iZj_list = get_data()
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wTc_values = estimate_poses(w_iZj_list, wRc_values)
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wRc_values, i_iZj_list = get_data()
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wTc_values = estimate_poses(i_iZj_list, wRc_values)
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print("**** Translation averaging output ****")
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print(wTc_values)
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print("**************************************")
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